Abstract
Background
Radiotherapy is the mainstay of treatment for head and neck cancer (HNC) but may induce various side effects on surrounding normal tissues. To reach an optimal balance, normal tissue complication probability (NTCP) models have been reported to predict the risk of radiation‐induced side effects in patients with HNC. However, the quality of study design, conduct, and analysis (i.e. risk of bias (ROB)), as well as the predictive performance of these models, remains to be evaluated.
Objectives
To identify, describe and appraise NTCP models to predict the risk of radiation‐induced side effects in patients with HNC.
Search methods
We searched Ovid MEDLINE, Embase and the World Health Organization International Clinical Trials Registry Platform from conception to January 2024. In addition, we screened references cited in the retrieved articles.
Selection criteria
Two review authors independently included articles reporting on the development and external validation of NTCP models to predict any type of radiation‐induced side effects in patients with HNC.
Data collection and analysis
One reviewer extracted data from each article and assessed their applicability and ROB, while another reviewer carefully verified the results. For models externally validated at least twice for the same outcome as their original developmental study, we performed qualitative analyses of the model performance. The GRADE system was not applied, since it has not been established for reviews of prognostic model studies.
Main results
Amongst 617 models developed from 152 articles, including 162,527 HNC patients, only 58 (9%) models from seven articles were judged to have low ROB and low concerns for applicability. No external validation was performed for 481 models (78%). For the remaining 136 models and six additional models which were not eligible for the present review, 193 external validations were performed in 48,852 patients with HNC in 39 articles.
Models for salivary‐related outcomes
Amongst 285 models, two models were externally validated at least twice.
The Beetz 2012b model for xerostomia six months after radiotherapy was validated in two studies. C‐statistics ranged from 0.70 to 0.74. Calibration performance was reported in one study. One validation study was rated as having low ROB in all domains, while the other was rated as having high ROB in the analysis domain.
The Cavallo 2021 model for acute xerostomia during radiotherapy for patients with nasopharyngeal cancer was externally validated in the same study, using two different types of cohorts. C‐statistics ranged from 0.68 to 0.73 and calibration plots were reported in both cohorts. Both validations were rated as having unclear ROB in the participants' domain because no detailed information about recruiting was provided.
Models for swallowing‐related outcomes
Amongst 85 models, two models were externally validated at least twice.
The Christianen 2012 model for dysphagia six months after radiotherapy was validated in five studies. C‐statistics ranged from 0.66 to 0.75. Calibration performance was assessed in all of them, while four of them were rated as having high ROB in the analysis domain due to the small sample size.
The Wopken 2014b model for tube feeding dependence six months after radiotherapy was validated in three external validation studies. C‐statistics ranged from 0.79 to 0.95, while calibration was evaluated in all studies. Due to the small size of the validation datasets, they were judged as having high ROB in the analysis domain.
Models for hypothyroidism
Of 72 models for hypothyroidism, two models were externally validated at least twice. In addition, there was another model which was not originally developed for patients with HNC, but validated in this domain.
The Boomsma 2012 for hypothyroidism within two years after radiotherapy was externally validated in two studies. C‐statistics ranged from 0.64 to 0.74, while only one study reported its calibration performance. Both validation studies were rated as having high ROB in the analysis domain.
The Ronjom 2013 model for radiation‐induced hypothyroidism was validated in three studies. C‐statistics ranged from 0.65 to 0.69 and calibration plots were reported in only one study. Two validation studies were judged as having high and the other was rated as having unclear ROB in the analysis domain.
The Cella 2012 model was originally developed to predict radiation‐induced hypothyroidism in patients with Hodgkin’s lymphoma. In two validation studies in patients with HNC, c‐statistics ranged from 0.65 to 0.68, but calibration performance was not reported. One validation study was rated as having a high ROB and the other was rated as being unclear in the analysis domain.
Models for brain and nerve‐related outcomes
Amongst 40 models, three were externally validated at least twice for temporal lobe injury.
The OuYang 2023 model, using deep learning in patients with nasopharyngeal cancer, was validated in the same paper using two different cohorts. C‐statistics ranged from 0.80 to 0.82, while calibration performance was assessed in both cohorts. Both validations were judged as having low ROB in all domains.
The Wen 2021 model was developed to predict temporal lobe injury in newly diagnosed nasopharyngeal cancer patients. The model was validated by OuYang 2023 using two cohorts and by Yang 2023a using four cohorts. C‐statistics ranged from 0.75 to 0.79, while calibration performance was not reported. The two validations by OuYang 2023 were judged as having unclear ROB in the analysis domain, and the four validations by Yang 2023a were judged as having high ROB in the analysis domain.
The Yang 2023a model was validated in the same paper using two cohorts. C‐statistics ranged from 0.79 to 0.81, while calibration performance was assessed in both cohorts. Both validations were judged as having high ROB in the analysis domain.
Models for outcomes related to hoarseness, fatigue, nausea‐vomiting, throat pain, aspiration
No models were externally validated at least twice.
Authors' conclusions
Amongst 617 developed models, only one‐fifth were externally validated, of which, ten models at least twice. These ten models showed acceptable discriminative performance at external validation. However, their calibration was not always reported. Furthermore, most validation studies were judged as having high ROB. In conclusion, this review shows the need for more external validation studies and improvement of the quality of conducting and reporting of prediction model studies.
Plain language summary
Which normal tissue complication probability (NTCP) models are available to predict the risk of radiation‐induced side effects after radiotherapy in patients with head and neck cancer, what is their quality, and what is their predictive performance?
Key messages
° Many NTCP models have been developed to predict unwanted effects following radiotherapy in head and neck cancer patients but most of them have not been sufficiently externally validated, that is, tested with patients who were not involved in the original model developmental study, to know how well they really predict the unwanted effects.
° For models tested in two or more studies in addition to their original model development studies, the quality of testing and reporting of their results was generally poor, so it is difficult to know how useful they might be.
° More and better designed studies are required to investigate this issue in the area of head and neck cancer.
How can we decide on the likelihood of having unwanted effects as a result of treatment?
The likelihood of having unwanted effects as a result of radiotherapy can be calculated by using so‐called NTCP models. NTCP models calculate the risk of radiation‐induced side effects based on information from the patient, their disease, and their treatment.
What did we want to find out?
Radiotherapy is the mainstay of treatment of patients with head and neck cancer. However, radiotherapy exposes healthy, sometimes crucial, parts of the head and neck region to radiation. This may result in damage to these normal organs, e.g. disturbed saliva production, which may have important consequences for the quality of life of head and neck cancer patients treated with radiotherapy. To reach an optimal balance between tumour control and preventing radiation‐induced side effects, normal tissue complication probability (NTCP) models can be helpful. These models predict the risk of radiation‐induced side effects based on information from the patient, their disease, and their treatment. There have been a substantial number of NTCP models for patients with head and neck cancer. We wanted to find out what the quality of study design, conduct, and analysis (i.e. risk of bias) is, and how well these models can predict the risk of radiation‐induced side effects.
What did we do?
We searched for studies that developed and/or validated NTCP models in patients with head and neck cancer.
What did we find?
In most of the 617 models developed from 162,527 patients in 152 identified articles, the quality of the models was not sufficient; and it has not been investigated how well they perform in new patients for 78% of these models. For the remaining 22% of the models, 193 external validations were found in 48,852 patients from 39 articles. There were only ten models with two or more external validations. The models were able to distinguish patients with and without the outcome well, but it was often unclear whether their predictions were in line with what was observed, because the latter was not always assessed and/or reported. Overall, the quality of most of these studies was low.
How up to date is the review?
The evidence is current to 8th January 2024.
Summary of findings
Summary of findings 1. Summary of findings.
| Outcome | Model | Study | Participants (n) | Model performance | |||||||
| Original | Updated | ||||||||||
| Patient | Events | C‐statistic | Calibration plot | Other | C‐statistic | Calibration plot | Other | ||||
| Xerostomia | Beetz (2012b)‐1 model (xerostomia 6 months after RT) | Development | Beetz (2012b)‐1 | 161 | 83 | 0.68 (0.60 to 0.76) | Figure 2 | Brier score: 0.1 R2: 0.13 discrimination slope: 0.1 HL test P = 0.84 NTCP curve: Figure 1 | NA | NA | NA |
| Validation | Blanchard (2016)‐3 | 94 | 36 | 0.74 (0.63 to 0.83) | NR | HL test P = 0.05 | NR | NR | HL test P = 0.08 | ||
| Langendijk (2021)‐1 | 438 | 200 | 0.70 | Figure S2 | NR | 0.97 | Figure S2 | NR | |||
| Langendijk (2021)‐2 | 669 | 338 | NA | NA | NR | 0.72 | Figure S2 | NR | |||
| Cavallo (2021)‐1 model (xerostomia during RT) | Development | Cavallo (2021)‐1 | 132 | 90 | 0.71 | Figure 1 | NR | NA | NA | NA | |
| Validation | Cavallo (2021)‐1 | 38 | 34 | 0.73 | Figure 6A | Calibration slope: 0.77 calibration‐in‐the‐large 0.38 NPV 0.65 PPV 0.75 (at a cut‐off of 65%) | NA | NA | NA | ||
| Cavallo (2021)‐2 | 93 | 77 | 0.68 | Figure 6B | Calibration slope: 0.50 calibration‐in‐the‐large: 0.53 NPV: 0.52 PPV: 0.69 (at a cut‐off of 65%) | NA | NA | NA | |||
| Dysphagia | Christianen (2012)‐1 model (RTOG grade 2–4 dysphagia 6 months after RT) | Development | Christianen (2012)‐1 | 354 | NR | 0.80 (0.75 to 0.85) | NR | HL test P = 0.19 scaled Brier score: 0.23 (0.15 to 0.31) R2 0.31: (0.21 to 0.41) discrimination slope: 0.22 (0.18 to 0.25) | NA | NA | NA |
| Validation | Blanchard (2016)‐2 | 89 | 27 | 0.71 (0.59 to 0.82) | NR | HL test P = 0.23 | NR | NR | HL test P = 0.66 | ||
| Christianen (2016)‐1 | 186 | 42 | 0.75 (0.68 to 0.82) | NR | HL test P = 0.74 scaled Brier score: 0.13 (0.03 to 0.22) R2: 0.21 (0.08 to 0.33) discrimination slope: 0.14 (0.1 to 0.18) | NR | NR | NR | |||
| Hansen (2019)‐1 | 284 | 94 | 0.68 | NR | Brier score: 0.47 | NR | Figure 1 | Brier score: 0.42 | |||
| Huynh (2023)‐1 | 239 | 75 | 0.66 | Figure 3 | Brier score: 0.15 | 0.72 | Figure 3 | Brier score: 0.15 | |||
| Langendijk (2021)‐1 | 354 | 109 | 0.74 | Figure S3 | Calibration‐in‐the‐large: 0.146 calibration slope 0.772 | 0.77 | Figure S3 | NR | |||
| Langendijk (2021)‐2 | 813 | 242 | NA | NA | NA | 0.81 | Figure S3 | Calibration‐in‐the‐large: 0.12; calibration slope: 1.2 | |||
| Wopken (2014b)‐1 model (tube feeding dependence 6 months after RT) | Development | Wopken (2014b)‐1 | 355 | 38 | Apparent: 0.88 Internal: 0.85 | Calibration slope 0.27 | Nagelkerke R2 0.4 | NA | NA | NA | |
| Validation | Blanchard (2016)‐1 | 89 | 4 | 0.95 (0.85 to 1.00) | NR | HL test P = 0.43 | NR | NR | NR | ||
| Kanayama (2018)‐1 | 7 | 122 | 0.79 (0.65 to 0.90) | Calibration‐in‐the‐large ‐0.99; calibration slope 0.97 | Discrimination slope: 0.18 (0.08 to 0.24) HL test P = 0.38 R2: 0.05 (‐0.01 to 0.38) | NR | NR | Likelihood ratio test P = 0.03 compared to the original model | |||
| Langendijk (2021)‐5 | 457 | 55 | 0.85 | Figure S4 | Calibration‐in‐the‐large: 0.42; calibration slope: 0.81 | 0.86 | Figure S4 | Calibration‐in‐the‐large: ‐0.001; calibration slope: 0.992 | |||
| Langendijk (2021)‐6 | 812 | 93 | NA | NA | NA | 0.85 | Figure S4 | Calibration‐in‐the‐large: 0.110; calibration slope: 1.080 | |||
| Hypothyroidism* | Boomsma (2012)‐1 model (clinical or subclinical hypothyroidism within two years after RT) | Development | Boomsma (2012)‐1 | 105 | 35 | 0.85 (0.78 to 0.92) | NR | NR | NA | NA | NA |
| Validation | Blanchard (2016)‐4 | 58 | 40 | 0.74 (0.57 to 0.91) | NR | HL test P = 0.01 | NR | NR | HL test P = 0.53 | ||
| Luo (2018)‐1 | 174 | 39 | 0.64 (0.56 to 0.71) | NR | NR | NR | NR | NR | |||
| Ronjom (2013)‐1 model (biochemical hypothyroidism anytime after RT) | Development | Ronjom (2013)‐1 | 203 | 35 | NR | NR | NR | NA | NA | NA | |
| Validation | Ronjom (2015)‐1 | 198 | 38 | NR | Calibration curve | Pearson cc 0.97 | NA | NA | NA | ||
| Luo (2018)‐4 | 174 | 39 | 0.65 (0.57 to 0.72) | NR | NR | NA | NA | NA | |||
| Zhu (2021)‐4 | 244 | 138 | 0.69 (0.63 to 0.75) | NR | NR | NA | NA | NA | |||
| Cella (2012)‐2 model (biochemical hypothyroidism anytime after RT) | Development | Cella (2012)‐2 | 53 | 22 | 0.87 (0.75 to 0.95) | NR | NR | NA | NA | NA | |
| Validation | Luo (2018)‐3 | 174 | 39 | 0.68 (0.60 to 0.75) | NR | NR | NA | NA | NA | ||
| Zhu (2021)‐3 | 244 | 138 | 0.65 (0.59 to 0.71) | NR | NR | NA | NA | NA | |||
| Temporal lobe injury | OuYang (2023)‐1 (temporal lobe injury after RT) | Development | OuYang (2023)‐1 | 6292 | 519 | 0.79 (0.77 to 0.81) | Supplementary Figure 3A | Time‐specific temporal lobe injury probabilities in Supplementary Figure 5A | NA | NA | NA |
| Validation | OuYang (2023)‐1 | 1930 | 174 | 0.82 (0.79 to 0.85) | Supplementary Figure 3C | Decision curve analysis in Supplementary Figure 4B. Time‐specific temporal lobe injury probabilities in Supplementary Figure 5C | NA | NA | NA | ||
| OuYang (2023)‐2 | 2780 | 164 | 0.80 (0.77 to 0.84) | Supplementary Figure 3D | Decision curve analysis in Supplementary Figure 4C; Time‐specific temporal lobe injury probabilities in Supplementary Figure 5D | NA | NA | NA | |||
| Wen (2021)‐1 model (temporal lobe injury after RT) | Development | Wen (2021)‐1 | 8194 | 989 | 0.78 (0.77 to 0.80) | Figure 2C | AUC at 5 years: 0.83 | NA | NA | NA | |
| Validation | OuYang (2023)‐3 | 1930 | 174 | 0.77 (0.73 to 0.80) | NR | NR | NA | NA | NA | ||
| OuYang (2023)‐4 | 2780 | 164 | 0.79 (0.75 to 0.82) | NR | NR | NA | NA | NA | |||
| Yang (2023a)‐1 | 5006 | 428 | 0.75 (0.73‐0.77) | NR | NR | NA | NA | NA | |||
| Yang (2023a)‐2 | 2144 | 163 | 0.78 (0.75‐0.81) | NR | NR | NA | NA | NA | |||
| Yang (2023a)‐3 | 1976 | 122 | 0.76 (0.71‐0.80) | NR | NR | NA | NA | NA | |||
| Yang (2023a)‐4 | 2072 | 132 | 0.75 (0.71‐0.79) | NR | NR | NA | NA | NA | |||
| Yang (2023)‐1 model (temporal lobe injury after RT) | Development | Yang (2023a)‐1 | 5006 | 428 | 0.78 (0.75‐0.80) | Figure 2B | Dosiomics nomogram in Figure 2A Decision curve analysis in Figure E5 | NA | NA | NA | |
| Validation | Yang (2023a)‐5 | 1976 | 122 | 0.81 (0.77‐0.84) | Figure 2B | Decision curve analysis in Figure E5 | NA | NA | NA | ||
| Yang (2023a)‐6 | 2072 | 132 | 0.79 (0.76‐0.83) | Figure 2B | Decision curve analysis in Figure E5 | NA | NA | NA | |||
| Hoarseness* | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ |
| Fatigue* | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ |
| Nausea‐vomiting* | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ |
| Throat pain* | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ |
| Aspiration* | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ |
* There are fewer than two external validations for models to predict the outcome.
Abbreviations: AUC: area under the curve cc : correlation coefficient EORTC: European Organisation for Research and Treatment of Cancer criteria HL: Hosmer‐Lemeshow test (P value) LRT: likelihood ratio test NA: not applicable NPV: negative predictive value NR: not reported NTCP: normal tissue complication probability PPV: positive predictive value RT: radiotherapy RTOG: Radiation Therapy Oncology Group
Background
Description of the health condition and context
Head and neck cancer is the sixth most common malignant neoplasm in the world (Gupta 2016). More than 90% of head and neck cancers are squamous cell carcinomas originating from the upper airway and digestive tract, typically in the oral cavity, larynx, or pharynx (Gupta 2016). Since squamous cell carcinomas, as well as other histological types of head and neck cancer, are mostly radio‐sensitive, radiotherapy (either alone or a combination of surgery or chemotherapy, or both) is the mainstay of treatment for head and neck cancer (De Felice 2018). Despite its effectiveness, the challenge of radiotherapy is how to avoid the risk of radiation‐induced side effects, by reducing the radiation dose to normal tissues. The relationship between the dose distributions for normal tissues and the risk of radiation‐induced side effects can be estimated by prediction models, often referred to as so‐called “normal tissue complication probability” (NTCP) models (Van der Laan 2012). Head and neck cancer radiotherapy may result in a wide spectrum of radiation‐induced side effects such as xerostomia (dry mouth syndrome), dysphagia (difficulty in swallowing), mucositis (inflammation of the mucous membrane), problems with taste, problems with speaking, trismus (restriction in mouth opening) and hearing loss. As these side effects have a significant impact on the more general dimensions of quality of life (Langendijk 2008), prevention of these side effects is crucial. Nowadays, more advanced radiation technologies, like proton therapy, have become available that are able to maintain tumour control but simultaneously are able to reduce irradiation to normal tissues. By using NTCP models, which include predictors related to radiation dose to normal tissues next to other non‐dose‐related predictors, it is possible to use such models for the so‐called model‐based selection for new or conventional radiotherapy therapy. In the model‐based selection, first, the radiation dose distribution in the target organ is calculated for both the new and conventional therapy. Then, the risk of radiation‐induced side effects is estimated for the new and conventional therapy by using NTCP models including predictors related to the radiation dose distribution. Thus, individual plan comparison between the new and conventional therapy can be made in each patient, in which the radiation dose distributions are translated to the risk of radiation‐induced side effects with both therapies (Figure). The difference in the risk of radiation‐induced side effects between the conventional and new therapy (∆NTCP) can then be used to determine if a clinically relevant benefit for the patient can be expected from new therapy instead of conventional therapy. The above approach is known as the model‐based approach (Langendijk 2013), and is currently used for model‐based selection of head and neck cancer patients for proton therapy in the Netherlands (Langendijk 2021).
1.
In the model‐based selection, individual plan comparison is made in each patient, in which the radiation dose distributions are translated to the risk of radiation‐induced side effects for protons and photons. The difference in the risk of radiation‐induced side effects between the photon and proton therapy (∆NTCP) can be calculated.
Description of the NTCP models under review
Various NTCP models for the prediction of various types of radiation‐induced side effects in patients with head and neck cancer (e.g. xerostomia, dysphagia, hearing loss, and hypothyroidism) have been developed (e.g. Beetz 2012a; Cheraghi 2017; Christianen 2016; Luo 2018; Wopken 2014a). These models estimate the future risk of radiation‐induced side effects in head and neck regions based on multiple predictors. These predictors are mainly classified as dosimetric and non‐dosimetric factors. Dosimetric factors are those related to radiation dose to the normal tissues. Non‐dosimetric factors include patient characteristics (e.g. age and gender), type/site/stage of cancer, and concurrent treatment. The purposes of these NTCP models for estimating an individual’s risk of these radiation‐induced side effects are as follows:
i) informing patients and their families about the possible side effects caused by radiotherapy;
ii) optimising dose distributions guided by the dose parameters included in the NTCP models (i.e. model‐based optimisation);
iii) selection of patients who may benefit most from newly developed techniques of radiotherapy.
Health outcomes
Radiation exposure to normal tissues surrounding the tumour may cause various types of side effects (e.g. xerostomia, oral mucositis, sticky saliva, dysphagia, oesophagitis, taste and hearing loss, aspiration, hoarseness, fatigue, trismus, and hypothyroidism), which may have significant impact on health‐related quality of life (Bansal 2004). Depending on the type of side effects, these may occur at various time points (during treatment and years after treatment) (Van der Veen 2017).
Why it is important to do this review
NTCP models for predicting side effects of radiotherapy in patients with head and neck cancer are crucial to identify those who are at the lowest risk of developing side effects from advanced radiation technologies such as proton therapy. Since there have been several NTCP models for each type of radiation‐induced side effect (Beetz 2012a; Cheraghi 2017; Christianen 2016; Luo 2018; Wopken 2014a), this review is important to identify all available NTCP models predicting radiation‐induced side effects, appraise their quality of study design, conduct, and analysis (i.e. risk of bias) and applicability, and summarise the current evidence on their predictive performance, obtained both from the model development and from model validation studies. Therefore, this review will be helpful to understand which model(s), and to which extent, may be applied for therapeutic decision‐making on the preferred radiation approach, which models should not be used for this purpose, and which models are promising but still require further investigation, e.g. testing their predictive accuracy in other patients or settings.
Objectives
To identify, describe and appraise NTCP models to predict the risk of radiation‐induced side effects in patients with head and neck cancer. Both model development and validation studies were included.
Methods
Criteria for considering studies for this review
Types of studies
As prespecified in the protocol (Takada 2021), based on the PICOT (Population, Intervention, Comparison, Outcome, Timing) of the review question (Table) (Debray 2017; Moons 2014; Moons 2019), we included studies which met all the following criteria:
1. PICOTS of the review question based on the CHARMS checklist.
| Population targeted | Patients with head and neck cancer undergoing current standard radiotherapy techniques (e.g. intensity modulated radiotherapy [IMRT] or volumetric modulated arc therapy [VMAT], 3D conformal radiotherapy and/or proton therapy) |
| Index model(s) | All available prognostic models predicting the risk of radiation‐induced side effects |
| Comparator model(s) | Not applicable |
| Outcome(s) to be predicted | All types of acute and late radiation‐induced side effects in head and neck regions |
| Timing of making a prediction and time span of the prediction | Just before starting radiotherapy. There were no restrictions on the prediction horizon. |
| Setting | Secondary and tertiary care |
IMRT: intensity modulated radiotherapy VMAT: volumetric modulated arc therapy
i) study design: all retrospective and prospective cohort and nested case‐control studies. Non‐nested case‐control studies were not eligible as this design is inappropriate for prediction model studies because cases are over‐represented in the study population, leading to misestimation of the intercept;
ii) study reports: original articles excluding conference proceedings;
iii) data source: studies that used existing routine care or registry data, or prospectively collected new data;
iv) aim: studies that aimed to develop, evaluate (internal and/or external validation) or update NTCP models to predict radiation‐induced side effects in patients with head and neck cancer who underwent radiotherapy. The timing of model usage was just before starting radiotherapy.
We did not apply any restriction based on outcome definition when selecting eligible validation studies. There was no restriction on language.
Targeted population
The targeted population consisted of patients with head and neck cancer undergoing definitive or postoperative radiotherapy either in combination, or not, with systemic agents (e.g. chemotherapy, cetuximab). All studies that included only a subgroup of this targeted population (e.g. including only patients with tongue cancer) were also eligible. Model development or validation studies which included this target population but combined with other target populations (such as patients with prostate cancer) were not eligible for this review. We only included studies that included patients treated with current standard radiation technologies, such as intensity modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), 3D conformal radiotherapy and/or proton therapy. There were no restrictions on healthcare setting, types of concurrent treatment, and types/stages of head and neck cancer.
Types of NTCP models
We included all NTCP models aimed at predicting radiation‐induced side effects, regardless of the predictors included, and the statistical techniques used, including logistic, ordinal, or polytomous regression, time‐to‐event or other machine learning modelling techniques. We also included all the studies that validated these NTCP models. Models not originally developed for patients with head and neck cancer were eligible if they had been validated for predicting radiation‐induced side effects in this population, due to their potential applicability in clinical practice.
Types of outcomes to be predicted
The outcomes of interest were all types of side effects caused by radiation exposure on normal organs in the head and neck region: xerostomia, oral mucositis, sticky saliva, dysphagia, oesophagitis, loss of taste, tinnitus, hearing loss, aspiration, hoarseness, fatigue, oral pain, pain in the throat, trismus, osteoradionecrosis, hypothyroidism, retinopathy, visual impairment, temporal lobe injury, carotid stenosis and second neoplasia. We used the definition of those outcomes defined in each article. Since the time period needed to develop these side effects may differ and as the incidence of side effects may progress or recover over time, we had no restrictions on the timing of the prediction horizon.
Search methods for identification of studies
Electronic searches
We conducted the search in the following databases: Ovid MEDLINE (from 1946) and Embase.com (from 1974 to 8 January 2024). To efficiently identify NTCP model studies, we used and modified the search filter described by Geersing and colleagues (Geersing 2012) for our purpose. In addition, we searched for ongoing trials using the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) which contains 17 resources, including Clinicaltrials.gov (8 January 2024). The search strategies for each database are listed in Appendix 1. We checked reference articles cited in the retrieved articles. Furthermore, clinical experts in the field (MT, JAL and RJHMS) were contacted to identify any relevant NTCP models we might have missed in our search. Models published in the grey literature (i.e. conference abstracts) were not considered for inclusion.
Data collection
Selection of studies
Two of twelve review authors (TT, MT, MS, EC, LA, ZD, JFN, JAAGD, LMM, DLI, JC, ES) independently screened each article identified by the search for eligibility on title and abstract. Then, the same review authors independently assessed the eligibility of potentially relevant studies by reading the full‐text articles. Any disagreement between the two review authors was resolved by discussion. We documented study selection in a flow chart as recommended in the PRISMA guidelines (Moher 2009).
Data extraction and management
In some references, multiple models were reported. And even in the same article, some models were only developed without external validation, while others were developed and externally validated. Thus, in this review, data extraction was performed based on the level of individual models and not on the level of the article. All information was extracted from published literature. Pairs from two of 12 review authors (TT, MT, MS, EC, LA, ZD, JFN, JAAGD, LMM, DLI, JC, ES) independently extracted the data in accordance with the CHARMS checklist (Moons 2014). These items provided information to assess the applicability of the studies to the review question and their risks of bias (see below). Any disagreement between the review authors was resolved by discussion. The data from some of the included studies used in the models is found in the 'Additional Tables' section, rather than in the section, Characteristics of included studies, as they contributed to the development of the models.
The following information was extracted using a data extraction form (Appendix 2 for model development and Appendix 3 for model validation) based on the CHARMS checklist (Moons 2014).
● General information: author, year of publication, journal, country, language;
● Source of data: study design, prospective or retrospective data collection, registry;
● Participants: participant eligibility criteria and recruitment method (e.g. consecutive participants, study location, number of centres, setting, inclusion and exclusion criteria);
● Outcomes to be predicted: definition and method for measurement of outcome, time of outcome occurrence, summary of duration of follow‐up, whether the assessment of the outcome was blinded from predictors included in the NTCP models;
● Candidate predictors: number and type of predictors (e.g. demographics [age and sex], patient complaint(s) at baseline, comorbidities, physical examination [baseline toxicity], laboratory testing, characteristics of cancer [stage (T and N) and location of cancer], and radiation dose to organs at risk, treatment modality, baseline complaints);
● Sample size: number of participants and number of participants with the outcome of interest;
● Missing data: number of participants with any missing value, handling of missing data (e.g. complete‐case analysis, imputation or other methods);
● Model development: modelling method (e.g. logistic or time‐to‐event modelling), assumption of applied modelling, method for selection of predictors for inclusion in multivariable modelling (e.g. clinically‐relevant predictors or pre‐selection based on univariable analyses), method for selection of predictors during multivariable modelling and its criteria, shrinkage methods (e.g. no shrinkage, uniform shrinkage, or penalised modelling);
● Prediction performance of the models: calibration (calibration plot, observed‐expected ratio [O:E ratio], calibration‐in‐the‐large [CITL] and slope, and Hosmer‐Lemeshow (HL) test) and discrimination (c‐statistic) measures with confidence intervals;
● Model evaluation: method used for validation of model performance (development dataset, internal validation [e.g. random split of original data, or resampling methods], external validation [e.g. temporal, geographical, different setting, or different researchers]);
● Results: all types of presentation of the NTCP models (e.g. basic, extended, or simplified), including regression coefficients, intercept and baseline survival;
● Interpretation and discussion: interpretation of presented models (confirmatory or exploratory), comparison with other studies, discussion of generalisability, strengths and limitations.
Assessment of risk of bias and applicability
We used the PROBAST tool for the assessment of risk of bias (ROB) and applicability of all models reported in the included studies (Moons 2019; Wolff 2019). ROB and applicability were assessed in four domains with two to nine signalling questions (Table). Each domain was judged as high, low or unclear ROB. Judgement of ROB was facilitated by the signalling questions which could be answered “yes”, “probably yes”, “probably no”, “no”, or “no information”. Concerns regarding applicability were rated similarly to ROB, but without signalling questions. Pairs from two of 12 review authors (TT, MT, MS, EC, LA, ZD, JFN, JAAGD, LMM, DLI, JC, ES) independently assessed ROB and applicability. Any disagreement between the authors was resolved by discussion or by consulting a third review author in another pair. For both ROB and applicability, the overall judgement was established as the following:
2. Assessment of risk of bias and applicability.
| 1. Participants | 2. Predictors | 3. Outcome | 4. Analysis |
| Signalling questions | |||
| 1.1 Were appropriate data sources used, e.g. cohort, randomised controlled trial, or nested case– control study data? | 2.1 Were predictors defined and assessed in a similar way for all participants? | 3.1 Was the outcome determined appropriately? | 4.1 Were there a reasonable number of participants with the outcome? |
| 1.2 Were all inclusions and exclusions of participants appropriate? | 2.2 Were predictor assessments made without knowledge of outcome data? | 3.2 Was a prespecified or standard outcome definition used? | 4.2 Were continuous and categorical predictors handled appropriately? |
| 2.3 Are all predictors available at the time when the model is intended to be used? | 3.3 Were predictors excluded from the outcome definition? | 4.3 Were all enroled participants included in the analysis? | |
| 3.4 Was the outcome defined and determined in a similar way for all participants? | 4.4 Were participants with missing data handled appropriately? | ||
| 3.5 Was the outcome determined without knowledge of predictor information? | 4.5 Was selection of predictors based on univariable analysis avoided? | ||
| 3.6 Was the time interval between predictor assessment and outcome determination appropriate? | 4.6 Were complexities in the data (e.g. censoring, competing risks, sampling of control participants) accounted appropriately? | ||
| 4.7 Were relevant model performance measures evaluated appropriately? | |||
| 4.8 Were model overfitting, underfitting, and optimism in model performance accounted for? |
|||
| 4.9 Do predictors and their assigned weights in the final model correspond to the reported multivariable analysis? |
|||
| Risk of bias | |||
| Selection of participants | Predictors of their assessment | Outcome or its determination | Analysis |
| Applicability | |||
| Included participants or setting does not match the review question. | Definition, assessment, or timing of predictors does not match the review question. | Its definition, timing, or determination does not match the review question. |
Low: when a model was judged as low in all domains of ROB or applicability;
High: when a model was judged as high in at least one domain of ROB or applicability;
Unclear: when a model was judged as unclear in one or more domains and as low in all the other domains of ROB or applicability.
Measures of prediction model performance
To evaluate the predictive performance of NTCP models, we extracted measures for discrimination and calibration.
Discrimination refers to the ability of NTCP models to differentiate between patients who experience the outcome of interest and those who do not. The area under the receiver operating characteristic (ROC) curve (AUC) and the concordance (c‐) statistic are the most commonly used measures for discrimination. In logistic regression modelling for binary outcomes, AUC and the c‐statistic are identical. The c‐statistic can also be applied to time‐to‐event models which take censoring into account (Debray 2017).
Calibration refers to the agreement between predicted and observed outcome risks. This measure is less frequently reported than discrimination. However, as the decision‐making process would be based on a probability estimated by NTCP models, calibration performance needs to be ensured. Calibration can be assessed visually using a calibration plot and quantified by the calibration‐in‐the‐large and calibration slope. The Hosmer‐Lemeshow (HL) goodness‐of‐fit test is often used to assess calibration, in which non‐significant results (i.e. P ≥ 0.05) indicate that there is evidence for poor calibration. However, the HL test is not recommended, and is in fact downgraded in PROBAST when reported as the sole measure of calibration because this test has limited suitability to evaluate poor calibration and is sensitive to the number of groups used in the test and the available sample size (Moons 2019; Wolff 2019).
Dealing with missing data
In case there was insufficient information in the included studies, we contacted the corresponding authors to ask for additional information that was necessary for our analysis. If necessary information was not available, we tried to estimate performance measures (e.g. c‐statistics, O:E ratio and their standard error) using the available information (Debray 2017).
Assessment of heterogeneity
We planned to quantitatively assess sources of heterogeneity in model performance for the identified NTCP models with at least five external validations. However, we identified only one such model. Thus, for models with two to four validations, we described the study population, predictors, definition and incidence of the predicted outcomes, and prediction horizons, which could affect and explain differences in model performance across validation studies.
Assessment of reporting deficiencies
Despite recommendations by current guidelines to report both discrimination and calibration measures (Collins 2015a), this information is often missing in prediction model publications. We described the reporting deficiencies in the included articles based on the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement (Collins 2015b; Moons 2015).
Data synthesis
Data synthesis and meta‐analysis approaches
For each type of radiation‐induced side effect, we provided a summary of the identified NTCP models that were externally validated at least twice for the outcome identical with the original study, including the following information: author, publication year, number of patients included, predictors, outcome definition, number of patients with the event, c‐statistic, O:E ratio, CITL and calibration slope. We performed meta‐analyses of the model performance if there were at least five external validations for one or more of the identified NTCP models, and if these external validation studies were sufficiently robust and comparable. A minimum of five studies was required to ensure appropriate coverage of confidence and prediction intervals in a random‐effects meta‐analysis (Partlett 2017). Using appropriate transformations (e.g. a logit transformation for c‐statistics) (Snell 2018), we reported the pooled performance measures with confidence intervals and prediction intervals based on a random‐effects approach (Debray 2019; Debray 2017).
Subgroup analysis and investigation of heterogeneity
The severity and frequency of radiation‐induced toxicity differ based on various variables (Van den Bosch 2021). Consequently, the performance of a model at external validation varies depending on patient, disease, and treatment characteristics. For instance, patients with advanced stage cancer typically require more aggressive treatment, including higher radiation doses and concurrent chemotherapy, which increases their susceptibility to radiation‐induced side effects. A model trained on patients with early stage cancer may under‐predict toxicity in advanced cases unless the model includes all relevant differences between those with early and advanced stage cancer as predictors. Therefore, we planned to compare the model performance at external validation in terms of the c‐statistic across patient subgroups by grouping studies based on:
1. Tumour location
Oral cavity vs. larynx vs. hypopharynx vs. oropharynx vs. nasopharynx
Larynx vs. pharynx vs. others;
2. Stage of cancer
Early stage (stage I‐II) vs. advanced stage (III‐IV);
3. Treatment modality
Primary vs. postoperative setting
Radiotherapy alone vs. radiotherapy in combination with systemic agents (chemotherapy and targeted therapy).
Unfortunately, we were not able to perform any of these analyses due to the small number of models externally validated and the insufficient number of studies that limited their inclusion into these prespecified categories, i.e. most studies included a mixture of these patient subgroups.
Sensitivity analysis
If there was a sufficient number of validation studies of the same model, we planned to perform sensitivity analyses using only studies with low ROB (i.e. rated as low ROB on all four PROBAST domains). However, due to the small number of external validations of the same models, we could not perform the sensitivity analyses.
Rating the certainty of evidence and summary of findings
Although we originally planned to use the most recent GRADE guidance for rating the certainty of evidence, it was not established for reviews of prediction model studies at the time of conducting this review. Therefore, we did not apply GRADE in this review. We summarised the characteristics and performance of models with two or more external validations in the summary of findings tables.
Results
In this section, we first describe the results of the search, and then, summarise the characteristics, ROB, and applicability concerns of developmental studies and external validation studies. Finally, we elaborate on models externally validated at least twice for the same outcome as in their developmental studies, as those models have the best potential to be applied in clinical practice. For those models, the description of the original model, external validation studies, their ROB and applicability concerns are summarised.
Results of the search
From Ovid MEDLINE and Embase.com databases, a total of 12,777 unique potentially relevant articles were identified by our search after removal of duplicates. Additionally, 217 ongoing trials were identified by tracing the WHO ICTRP, but none were found to be eligible for inclusion. Of 12,777 articles, 11,885 were excluded based on title and abstract screening, leaving 892 articles for full‐text screening. During the full‐text screening, 720 articles were excluded. From the 172 remaining articles, data were extracted from 152 articles that described model development, including development only (n = 67), development with internal validation (n = 66), development with external validation (n = 6) validation, or with both internal and external validation (n = 13), and 20 articles that reported solely on the external validation of an existing model (i.e. without development of their own model) (Figure). There are no studies awaiting classification.
2.
Study flow diagram
In total, there were 617 developed models (Figure). Out of 617 developed models, 136 models were externally validated at least once either by the same author in the same/another publication or by another author in a total of 178 external validations. In addition, there were six models from three studies that were validated in head and neck cancer patients in 15 external validations, but for which their original development studies were not included in this review because these original studies were not eligible (Cella 2012 [ineligible study population], Van Dijk 2018b [assessment of added value of a certain predictor], Vogelius 2011 [not an original study]). In total, 193 external validations of 142 models were identified. No external validation was performed for 481 models (78%). The details of individual models with and without external validation are summarised in https://doi.org/10.17605/OSF.IO/MBHCG and the details of each external validation are summarised in https://doi.org/10.17605/OSF.IO/MBHCG.
Characteristics of included studies
A general overview of the model development and external validations has been described in the following sections. Developed models and external validations are grouped based on the predicted outcomes. Table provides an overview of predicted outcomes by main outcome groups. Additionally, details of the models that were externally validated by ≥ two times for the same outcome in the original developmental study are given in the section ‘Findings’.
3. Grouping of models based on outcome predicted amongst developed models and external validations.
| Outcome group | Outcome | Developed models n (% | External validations n (% |
| Total | 617 (100) | 193 (100) | |
| Salivary‐related | Xerostomia | 243 (39) | 59 (31) |
| Stimulated salivary function | 13 (2) | ||
| Loss of taste | 10 (2) | 4 (2) | |
| Salivary gland toxicity | 6 (1) | ||
| Sticky saliva | 6 (1) | 5 (3) | |
| Saliva amount | 2 (0) | ||
| Parotid Gland shrinkage | 2 (0) | ||
| Salivary flow | 2 (0) | ||
| Salivary duct inflammation | 1 (0) | ||
| Swallowing‐related | Dysphagia | 67 (11) | 29 (15) |
| Tube feeding dependence | 10 (2) | 6 (3) | |
| Aspiration | 5 (1) | 4 (2) | |
| Duration of tube feeding dependence | 1 (0) | ||
| Grade >=3 dysphagia and/or PEG insertion | 1 (0) | ||
| Esophageal stricture | 1 (0) | ||
| Hypothyroidism | Hypothyroidism | 68 (11) | 37 (19) |
| Clinical Hypothyroidism | 4 (1) | 7 (4) | |
| Mucosa‐related | Oral mucositis | 34 (6) | 10 (5) |
| Excessive mucus | 4 (1) | ||
| Nasal cavity stenosis and atresia | 1 (0) | ||
| Radiation‐induced necrosis | 1 (0) | ||
| Brain and nerve‐related | Hearing loss | 13 (2) | 1 (1) |
| Temporal lobe injury | 13 (2) | 14 (7) | |
| Temporal lobe necrosis | 4 (1) | ||
| Optic neuropathy | 3 (0) | ||
| Tinnitus | 2 (0) | ||
| Hypoglossal nerve palsy | 1 (0) | ||
| Increase of >=10 dB in 1‐, 2‐, and 4‐kHz pure tone | 1 (0) | ||
| Brainstem necrosis | 1 (0) | ||
| Post‐treatment pure tone average | 1 (0) | ||
| Radiation‐induced brain injury | 1 (0) | ||
| Osteoradionecrosis | Osteoradionecrosis | 19 (3) | |
| Trismus | Trismus | 11 (2) | |
| Jaw dysfunction | 3 (0) | ||
| Weight loss and denutrition | Weight loss | 9 (1) | 2 (1) |
| Denutrition | 2 (0) | ||
| Lethal toxicities | Carotid blown syndrome | 9 (1) | |
| Lethal nasopharyngeal necrosis | 1 (0) | ||
| Skin‐related | Dermatitis | 7 (1) | |
| Skin acute toxicity | 2 (0) | ||
| Speech | Speech problems | 6 (1) | 2 (1) |
| Hoarseness | 2 (0) | 2 (1) | |
| General | Fatigue | 2 (0) | 2 (1) |
| Nausea and vomiting | 2 (0) | 2 (1) | |
| Late toxicity | 2 (0) | ||
| Multisymptom severity | 1 (0) | 1 (1) | |
| Laryngeal toxicities | Larynx toxicity | 4 (1) | |
| Larynx edema grade | 2 (0) | ||
| Pain | Jaw pain | 2 (0) | 2 (1) |
| Oral pain | 2 (0) | 2 (1) | |
| Throat pain | 2 (0) | 2 (1) | |
| Neck fibrosis | Neck fibrosis | 5 (1) |
*composite MD Anderson Symptom Inventory Head and Neck Module (MDASI‐HN) symptom score
Abbreviations: CTCAE: common terminology criteria for adverse events dB: decibel PEG: percutaneous endoscopic gastrostomy
Characteristics of developed models and reporting deficiencies
An overview of the developed models is given in Table, while the characteristics of each of the developed models are given in https://doi.org/10.17605/OSF.IO/MBHCG. Almost half of the developed models aimed to predict toxicities related to salivary gland function (n = 285, 46%), followed by swallowing‐related toxicities (n = 85, 14%), and hypothyroidism (n = 72, 12%) (Figure).
4. Characteristics of study setting, participants and treatment for all developed models (n = 617) and external validations (n = 193).
| Variable N (%) | Developed models | External validations | |
| Number of centers | < 5 | 562 (91) | 162 (84) |
| 5‐10 | 15 (2) | 1 (1) | |
| > 10 | 13 (2) | 0 (0) | |
| Not reported | 27 (4) | 30 (16) | |
| Region | Europe | 209 (34) | 93 (48) |
| North America | 208 (34) | 21 (11) | |
| Asia | 174 (28) | 53 (27) | |
| Combination | 20 (3) | 0 (0) | |
| Australia | 3 (0) | 0 (0) | |
| Not reported | 3 (0) | 26 (13) | |
| Data source | Prospective cohort | 220 (36) | 94 (49) |
| Retrospective cohort | 342 (55) | 48 (25) | |
| Randomised trial participants | 33 (5) | 31 (16) | |
| Nested case‐control | 11 (2) | 0 (0) | |
| Cross‐sectional | 0 (0) | 6 (3) | |
| Not reported | 11 (2) | 14 (7) | |
| Recruitment | Consecutive | 272 (44) | 83 (43) |
| Non‐consecutive | 3 (0) | 6 (3) | |
| Not reported | 342 (55) | 104 (54) | |
| Predicted outcome | Salivary‐related | 285 (46) | 68 (35) |
| Swallowing‐related | 85 (14) | 39 (20) | |
| Brain and nerve‐related | 40 (6) | 15 (8) | |
| Hypothyroidism | 72 (12) | 44 (23) | |
| Mucosa‐related | 40 (6) | 10 (5) | |
| Trismus | 14 (2) | 0 (0) | |
| Weight loss and denutrition | 11 (2) | 2 (1) | |
| Lethal toxicities | 10 (2) | 0 (0) | |
| General | 7 (1) | 5 (3) | |
| Laryngeal toxicities | 6 (1) | 0 (0) | |
| Pain | 6 (1) | 6 (3) | |
| Neck fibrosis | 5 (1) | 0 (0) | |
| Speech | 8 (1) | 4 (2) | |
| Skin‐related | 9 (1) | 0 (0) | |
| Osteoradionecrosis | 19 (3) | 0 (0) | |
| Number of patients included in analysis | Median (range) | 155 (16 ‐ 8194) | 164 (19 ‐ 2503) |
| Interquartile range | 60 ‐ 222 | 49 ‐ 395 | |
| Not reported | 0 (0) | 4 (2) | |
| Number of patients with outcome | Median (range) | 48 (4 ‐ 989) | 43 (3 ‐ 351) |
| Interquartile range | 25 ‐ 106 | 27 ‐ 107 | |
| Not applicable | 33 (5) | 1 (1) | |
| Not reported | 72 (12) | 12 (6) | |
| Number of predictors | Median (range) | 2 (1 ‐ 749) | 2 (1 ‐ 23) |
| Interquartile range | 1 ‐ 4 | 1 ‐ 3 | |
| Not applicable | 11 (2) | 0 (0) | |
| Not reported | 92 (15) | 15 (8) | |
| Age | Mean (range) | 34 ‐ 67 | 49 ‐ 65 |
| Median (range) | 10 ‐ 67 | 45 ‐ 63 | |
| Not reported | 157 (25) | 64 (33) | |
| Sex | Male % (range) | 77 (26 ‐ 97) | 74 (61 ‐ 92) |
| Not reported | 104 (17) | 34 (18) | |
| Tumour site | Oral cancer % (range) | 9 (0 ‐ 100) | 7 (0 ‐ 52) |
| Not reported | 70 (11) | 58 (30) | |
| Laryngeal cancer % (range) | 10 (0 ‐ 84) | 22 (0 ‐ 50) | |
| Not reported | 75 (12) | 62 (32) | |
| Hypopharyngeal cancer % (range) | 5 (0 ‐ 34) | 8 (0 ‐ 100) | |
| Not reported | 68 (11) | 59 (31) | |
| Oropharyngeal cancer % (range) | 43 (0 ‐ 100) | 30 (0 ‐ 100) | |
| Not reported | 56 (9) | 51 (26) | |
| Nasopharyngeal cancer % (range) | 26 (0 ‐ 100) | 37 (0 ‐ 100) | |
| Not reported | 78 (13) | 57 (30) | |
| Cancer stage | Stage 1 % (range) | 6 (0 ‐ 62) | 8 (0 ‐ 27) |
| Not reported | 408 (66) | 158 (82) | |
| Stage 2 % (range) | 21 (0 ‐ 51) | 18 (0 ‐ 43) | |
| Not reported | 407 (66) | 156 (81) | |
| Stage 3 % (range) | 31 (0 ‐ 68) | 36 (13 ‐ 60) | |
| Not reported | 403 (65) | 161 (83) | |
| Stage 4 % (range) | 40 (0 ‐ 100) | 35 (20 ‐ 83) | |
| Not reported | 401 (65) | 160 (83) | |
| Radiotherapy technique | IMRT % (range) | 48 (0 ‐ 100) | 53 (0 ‐ 100) |
| Not reported | 120 (19) | 82 (42) | |
| VMAT % (range) | 33 (0 ‐ 100) | 58 (0 ‐ 100) | |
| Not reported | 115 (19) | 107 (55) | |
| 3D‐conformal % (range) | 16 (0 ‐ 100) | 5 (0 ‐ 45) | |
| Not reported | 99 (16) | 103 (53) | |
| Brachytherapy % (range) | 0 (0 ‐ 14) | 0 (0 ‐ 0) | |
| Not reported | 126 (20) | 137 (71) | |
| Proton therapy % (range) | 1 (0 ‐ 100) | 9 (0 ‐ 100) | |
| Not reported | 108 (18) | 130 (67) | |
| Target radiation dose | ≥ 66Gy | 204 (33) | 30 (16) |
| Case mix of different target doses | 251 (41) | 86 (45) | |
| < 66Gy | 62 (10) | 0 (0) | |
| SBRT | 13 (2) | 0 (0) | |
| Not reported | 87 (14) | 77 (40) | |
| Radiotherapy fractionation | Conventional | 353 (57) | 45 (23) |
| Case mix with different fractionation schemes | 91 (15) | 59 (31) | |
| Non‐conventional fractionation schemes | 42 (7) | 7 (4) | |
| Not reported | 131 (21) | 82 (42) | |
| Systemic treatment | Concurrent chemotherapy % (range) | 70 (0 ‐ 100) | 62 (22 ‐ 100) |
| Not reported | 173 (28) | 40 (21) | |
| Induction chemotherapy % (range) | 22 (0 ‐ 100) | 13 (0 ‐ 47) | |
| Not reported | 465 (75) | 135 (70) | |
| Adjuvant chemotherapy % (range) | 6 (0 ‐ 67) | 1 (0 ‐ 4) | |
| Not reported | 496 (80) | 161 (83) | |
| Concurrent molecular therapy % (range) | 16 (0 ‐ 82) | 4 (0 ‐ 10) | |
| Not reported | 527 (85) | 109 (56) |
Abbreviations: IMRT: intensity modulated radiotherapy VMAT: volumetric modulated arc therapy
3.
Number of developed models and external validations per outcome group
Source of data, participants
Most of the models were developed in retrospective cohort studies (n = 342, 55%) involving generally less than five centres (n = 562, 91%). Models were most frequently reported from Europe (n = 209, 34%), followed by North America (n = 208, 34%) and Asia (n = 174, 28%). The patient recruitment method was consecutive for 272 models (44%), non‐consecutive for three models (1%) and not reported for 342 models (55%).
The median number of participants used for model development was 155 (interquartile range (IQR): 60–222, range: 16–8194; reported in all models) with a median number of 48 participants with an event (IQR: 25–106, range: 4–989); not reported for 72 models (12%), and not applicable (e.g. continuous outcome like salivary flow) for 33 models (5%). The range of the mean and median age of the participants was 34–67 and 10–67 years, respectively; while for 157 models (25%), the age of the participants was not reported. The proportion of male participants varied between 26% and 97% in 513 models (83%) in which the sex of the participants was reported.
The population consisted of patients with a mix of different tumour sites in 321 models (52%); only nasopharyngeal cancer in 111 (18%), oropharyngeal in 135 (22%), oral cavity in 2 (1%) and the tumour site was not reported in 47 (8%). Tumour stage reporting was available for most models, with stage 1, 2, 3 and 4 distributions reported in 408 (66%), 407 (66%), 403 (65%) and 401 (65%) models, respectively. Consequently, most models did not target a specific tumour site or stage.
Most models were developed using patients treated with a mix of different radiotherapy techniques (n = 182, 29%) or IMRT (n = 162, 26%), followed by VMAT (n = 131, 21%), 3D‐conformal radiotherapy (n = 54, 9%) and proton therapy (n = 2, <1%). In 86 models (14%), the radiotherapy technique was not reported. Patients were treated using conventional fractionation for most models (n = 353, 57%), generally with a target dose of either ≥ 66 Gy (n = 204, 33%) or a mix of different target doses (n = 251, 41%). In most models (n = 461, 75%), systemic treatment was reported to be administered in the setting of various combinations of concurrent/induction/adjuvant/molecular targeted therapy. No systemic treatment was given in 12 models (2%), whereas information about systemic treatment was not reported in 144 models (23%). In summary, a broad population of patients with head and neck cancer was included in the development of most models.
Predictors, analysis
The characteristics of the analysis and model performance of the 617 models are given in Table. The median number of predictors included in the models was 2 (IQR: 1‐4, range: 1‐749; not reported in 92 models (15%) and not applicable for 11 models (2%) developed using uncountable predictors such as 3D dose distributions). The number of events per variable was ≥ 20 in 204 (33%), 10‐19 in 83 (13%) and < 10 in 95 (15%) models.
5. Characteristics of analysis and model performances of all model development studies (n = 617) and external validations (n = 193).
| Variable N (%) | Developed models | External validations | |
| Total | 617 (100) | 193 (100) | |
| Modelling technique | Logistic regression | 269 (44) | |
| LKB model | 32 (5) | ||
| Machine learning | 91 (15) | ||
| Time‐to‐event model | 17 (3) | ||
| Others | 204 (33) | ||
| Not reported | 4 (1) | ||
| Handling of missing data | Complete case analysis | 231 (37) | 36 (19) |
| Multiple imputation | 52 (8) | 51 (26) | |
| Others | 174 (28) | 5 (3) | |
| Not reported | 160 (26) | 101 (52) | |
| Event per variable | ≥ 20 | 204 (33) | |
| < 10 | 95 (15) | ||
| ≥ 10 & < 20 | 83 (13) | ||
| Not reported | 235 (38) | ||
| Handling of continuous data | Linear | 278 (45) | |
| Non‐linear transformation | 208 (34) | ||
| Categorized | 54 (9) | ||
| Not applicable | 4 (1) | ||
| Not reported | 73 (12) | ||
| Selection of predictors before modelling | All candidate predictors were used | 303 (49) | |
| Univariable analysis | 85 (14) | ||
| Multicollinearity | 60 (10) | ||
| Others | 122 (20) | ||
| Not applicable | 34 (6) | ||
| Not reported | 13 (2) | ||
| Selection of predictors during modelling | Forward selection | 90 (15) | |
| Lasso penalization | 88 (14) | ||
| Backward selection | 32 (5) | ||
| Others | 67 (11) | ||
| Not applicable | 295 (48) | ||
| Not reported | 45 (7) | ||
| Internal validation technique | Resampling | 98 (16) | |
| Cross validation | 167 (27) | ||
| Random split | 47 (8) | ||
| Non‐random split | 2 (0) | ||
| Not applicable | 302 (49) | ||
| Not reported | 1 (0) | ||
| Shrinkage technique | Uniform shrinkage | 51 (8) | |
| Penalization | 63 (10) | ||
| Others | 2 (0) | ||
| Not applicable | 472 (76) | ||
| Not reported | 29 (5) | ||
| Model presentation | Full mathematical equation | 314 (51) | |
| Part of mathematical equation | 65 (11) | ||
| Graphical presentation | 58 (9) | ||
| Scoring system | 11 (2) | ||
| Not reported | 169 (27) | ||
| Calibration in apparent performance* | Reported | 79 (13) | 40 (21) |
| Not applicable | 236 (38) | 20 (10) | |
| Not reported | 302 (49) | 133 (69) | |
| C‐statistic in apparent performance* | Reported | 269 (44) | 183 (95) |
| Range | 0.55 ‐ 1.00 | 0.19 ‐ 0.96 | |
| Not applicable | 244 (40) | 3 (2) | |
| Not reported | 104 (17) | 7 (4) | |
| Calibration in internal validation or updated model† | Reported | 41 (7) | 14 (7) |
| Not applicable | 302 (49) | 161 (83) | |
| Not reported | 274 (44) | 18 (9) | |
| C‐statistic in internal validation or updated model† | Reported | 284 (46) | 26 (13) |
| Range | 0.36 ‐ 0.94 | 0.38 ‐ 0.97 | |
| Not applicable | 302 (49) | 162 (84) | |
| Not reported | 31 (5) | 5 (3) |
*For external validations, calibration and c‐statistics of the original model in external validation
†For external validations, calibration and c‐statistics of the updated model in external validation
The handling of the missing data was not reported in 160 models (26%), while the most common method was complete case analysis (n = 231, 37%). Logistic regression (n = 269, 44%) and machine learning (n = 91, 15%) modelling were the most common modelling techniques. Internal validation was performed in half of the models (n = 315, 51%) and done mostly using cross validation (n = 167, 27%). Just over half of the models were presented as a full mathematical equation including both intercept/baseline hazard and coefficients (n = 314, 51%).
Model performance
Calibration and discrimination of the apparent model were reported for 79 (13%) and 269 (44%) models, respectively. The c‐statistics values ranged between 0.55 and 1.00. Furthermore, of the 315 models that were assessed for their internal validation, calibration after internal validation was reported only in 41 (13%) models, while c‐statistics after internal validation were reported in 284 (90%) models, which varied between 0.36 and 0.94.
Characteristics of external validations and reporting deficiencies
One hundred and thirty‐six of the 617 developed models were validated in 178 external validations. In addition, 15 external validations of six models were included in the analyses because the external validation was performed in patients with head and neck cancer, despite the fact that their original developmental studies were not eligible for this review (Cella 2012; Van Dijk 2018b; Vogelius 2011). Most external validations were performed by the same author/group who developed the original model (n = 131, 68%). Almost one‐third of the external validations (32%) were performed in two articles by Van den Bosch and colleagues and Buettner and colleagues, with 36 and 25 external validations for the models which were developed by the same groups, respectively (Buettner 2012; Van den Bosch 2021).
External validation was most often performed for models that predicted outcomes related to saliva (n = 68, 35%), followed by hypothyroidism (n = 44, 23%) and outcomes related to swallowing (n = 39, 20%) (Figure). No external validations were found for models predicting outcomes related to trismus, lethal toxicities, laryngeal toxicities, neck fibrosis, skin‐related outcomes and osteoradionecrosis.
An overview of external validations, as well as frequencies of items that were not reported, is given in Table. In addition, reporting deficiencies for models with at least two external validations are demonstrated in Appendix 4.
Source of data, participants
External validations were most often conducted using prospective cohorts (n = 94, 49%) involving generally fewer than five centres (n = 162, 84%) from Europe (n = 93, 48%). The patient recruitment method was consecutive in 83 (43%) and not reported in 104 external validations (54%).
The median number of participants used for analysis was 164 (not reported in 4 (2%)) and the median number of participants with outcomes was 43 (not reported in 12 (6%)). The range of the mean and median age of the participants was 49–65 and 45–63 years, respectively; while in 64 external validations (33%), the age of the participants was not reported. The proportion of male participants varied between 61% and 92% in 159 external validations (82%), in which the sex of the participants was reported.
Thirty‐four of the 193 external validations (18%) included patients with a specific tumour site (oropharynx in 4, nasopharynx in 27, hypopharynx in 1 and other tumour sites in 2), while 120 (62%) external validations included multiple tumour sites and those not reported in 39 (20%). Disease stage was specified in 38 (20%) validations, which included patients with different disease stages.
Most external validations were performed using patients treated with a mix of different radiotherapy techniques (n = 72, 37%) or IMRT (n = 49, 25%), followed by VMAT (n = 9, 5%), and proton therapy (n = 5, 3%). However, radiotherapy techniques were not reported in 30% of the external validations, fractionation in 42% and target dose in 40%. In 153 external validations (79%), systemic treatment was administered in various settings, and this was not reported in the remaining 40 (21%), meaning most external validations focused on a head and neck patient population treated with combined radiotherapy and systemic treatment.
Predictors, analysis
The characteristics of the predictors, analysis and model performance of the 193 external validations are given in Table. Models that were externally validated included a median of two predictors (range: 1–23). Handling of missing data was not reported in 101 (52%) validations, while multiple imputation was the most commonly used technique (n = 51, 26%), followed by complete‐case analysis (n = 36, 19%).
Model performance
In 162 external validations (84%), validation was done using the original model without any model update. For original model performance, calibration was reported only in 40 (21%) and discrimination in 183 (95%). C‐statistics ranged between 0.19 and 0.96. In 31 external validations, the model was updated by recalibration‐in‐the‐large (intercept adjustment) in five; recalibration (adjustment of intercept and slope) in one; and model revision (re‐estimation of all coefficients) in 25, in which calibration and c‐statistics after model update were reported in 14 (45%) and 26 (84%) validations, respectively. The c‐statistics after model update ranged between 0.38 and 0.97.
Risk of bias assessment of models based on included studies
Risk of bias and applicability concerns for developed models
Of the 617 developed models, most had low ROB for the participants (81%), predictors (72%), and outcomes (82%) domain of PROBAST assessment. However, most models (79%) had high ROB for the analysis domain, due to lack of accounting for model overfitting and optimism in model performance, inappropriate model performance measures evaluation and missing data handling, and few participants with the outcome (Figure). The overall ROB was high in 79%, low in 9% and unclear in 11% of the models (Figure).
4.
Assessment of developed models in terms of risk of bias in the analysis domain of the PROBAST
5.
Assessment of risk of bias and applicability concerns of developed models (n = 617) using PROBAST
Similar to ROB, most of the models had low concerns of applicability in the participants (85%), predictors (97%) and outcomes (82%) domains. The overall applicability concern was high in 7%, low in 69% and unclear in 24% of the models (Figure).
ROB assessment showed that the 58 models published by Hansen for oral mucositis (Hansen 2020); Wen for temporal lobe injury (Wen 2021); Sheikh for xerostomia (Sheikh 2019); Wopken for tube feeding dependence Wopken 2014a); Hamada for acute dermatitis (Hamada 2023); Van den Bosch for dysphagia, aspiration, xerostomia, sticky saliva, loss of taste, mucositis, hoarseness, speech problems, oral pain, throat pain, jaw pain, weight loss, nausea and vomiting, and fatigue (Van den Bosch 2021); and Van Rijn‐Dekker for xerostomia (Van Rijn‐Dekker 2023) had the highest quality amongst all 617 developed models. ROB and applicability concerns of each of the 617 developed models are shown in https://doi.org/10.17605/OSF.IO/MBHCG, Appendix 5 and Appendix 6.
Risk of bias and applicability concerns for external validations
Of the 193 external validations, most had low ROB for the participants (69%), predictors (99%) and outcome (87%) domain of the PROBAST tool. Similar to the developed models, the analysis domain was considered at high ROB for most external validations (68%). The overall ROB was high in 70%, low in 27% and unclear in 3% of the external validations (Figure).
6.
Assessment of risk of bias and applicability concerns of external validations (n = 193) using PROBAST
The overall assessment resulted in a low applicability concern in most external validations (74%), whereas it was high in 2% (Figure). ROB and applicability concerns of each of the 193 external validations are shown in https://doi.org/10.17605/OSF.IO/MBHCG, Appendix 7 and Appendix 8
Findings
We identified 142 NTCP models that had been externally validated at least once, regardless of the outcome definition. Amongst these, ten models were externally validated at least twice using the same outcome definition as in their original development studies. Of these ten models, two predicted salivary‐related outcomes, two predicted swallowing‐related outcomes, three predicted hypothyroidism, and three predicted temporal lobe injury. No models with ≥ two external validations were identified for other outcomes.
Models with ≥ 2 external validations for salivary‐related outcomes
Amongst 285 models developed to predict salivary‐related outcomes, two models with ≥ two external validations were identified.
Beetz (2012b)‐1 (xerostomia)
Description
The Beetz (2012b)‐1 model predicts xerostomia in patients with head and neck cancer treated with IMRT with/without chemotherapy (Beetz 2012b) (Table). The outcome was defined as moderate‐to‐severe xerostomia based on the EORTC QLQ HN35 six months after radiotherapy. The model was developed using a prospective cohort of 161 patients from two European centres. The model showed a c‐statistic of 0.68 (95% confidence interval [CI] 0.60 to 0.76) with good calibration performance. No internal validation was performed (Table).
6. Characteristics of included studies for Beetz (2012b)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Beetz (2012b)‐1 | Development | Beetz (2012b) | NR | 2, Europe | Prospective | Consecutive | 83/161 | Median: 61, range 32‐85 | Male: 71% | Oral cavity: 35%, larynx: 37%, hypopharynx: 12%, oropharynx: 7%, nasopharynx: 1%, others: 8% | NR | IMRT | 70 Gy conventional fractionation | Concurrent: 33%, molecular: 6% |
| Validation | Blanchard (2016)‐3 | 2011‐2015 | 1, North America | Prospective | Unclear | 36/94 | Median: 60 IQR: 19‐92 | Male: 70% | Hypopharynx: 3%, oropharynx: 45%, nasopharynx: 14%, others: 38% | NR | Proton: 100% | NR | Concurrent: 56%, induction: 24%, molecular: 10% | |
| Langendijk (2021)‐1 | NR | 1, Europe | Prospective | Consecutive | 200/438 | > 65 years: 41% | Male: 76% | Oral cavity: 6%, larynx: 34%, oropharynx: 38%, nasopharynx: 3%, others: 19% | NR | NR | Conventional fractionation | Concurrent: 39% | ||
| Langendijk (2021)‐2 | NR | 1, Europe | Prospective | Consecutive | 338/669 | > 65 years: 38% | Male: 73% | Oral cavity: 6%, larynx: 39%, oropharynx: 38%, nasopharynx: 4%, others: 13% | NR | NR | Conventional fractionation | Concurrent: 39% | ||
Abbreviations: IMRT: intensity modulated radiotherapy IQR: interquartile range NR: not reported RT: radiotherapy
7. Model performance for Beetz (2012b)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Beetz (2012b)‐1 | Development | Beetz (2012b)‐1 | 0.68 (0.60‐0.76) | Figure 2 | Brier score: 0.1, R2: 0.13, discrimination slope: 0.1, HL test P = 0.84, NTCP curve: Figure 1 | NA | NA | NA | NA |
| Validation | Blanchard (2016)‐3 | 0.74 (0.63‐0.83) | NR | HL test P = 0.05 | Complete model revision | NR | NR | HL test P = 0.08 | |
| Langendijk (2021)‐1 | 0.7 | Figure S2 | Complete model revision | 0.97 | Figure S2 | NR | |||
| Langendijk (2021)‐2 | NA | NA | Complete model revision | 0.72 | Figure S2 | NR | |||
Abbreviations: HL: Hosmer‐Lemeshow NA: not applicable NR: not reported NTCP: normal tissue complication probability
External validation
The Beetz (2012b)‐1 model was validated in two studies (Blanchard 2016; Langendijk 2021). Blanchard 2016 validated the model in a prospective North American single centre cohort of 94 NPC patients, resulting in a c‐statistic of 0.74 (95% CI 0.63 to 0.83) and a P value in an HL test of 0.05. They also performed a complete model revision by re‐estimating all coefficients, resulting in a P value in an HL test of 0.08. This validation study included only patients who underwent proton therapy, while the original developmental study included those who underwent IMRT. Langendijk 2021, which included the same author group as in the original developmental study, validated the model using a prospective cohort at a European centre, with a c‐statistic of 0.7 and good calibration. They also updated the model by re‐estimating coefficients for all predictors using both development and validation cohorts. The updated model showed a c‐statistic of 0.72 with good calibration performance (Table).
Risk of bias
The original developmental study was judged as low in all domains except the analysis domain, which was judged as having high ROB, due to an insufficient number of patients, inappropriate handling of continuous variables, and no internal validation to address optimism (Table). Langendijk 2021 was rated as low in all domains, while Blanchard 2016 was judged as high in the analysis domain due to the small number of outcome events in the dataset.
8. Risk of bias (PROBAST) assessment of Beetz (2012b)‐1.
| Model | Study | PROBAST ‐ Risk of bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Beetz (2012b)‐1 | Development | Beetz (2012b)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low |
| Validation | Blanchard (2016)‐3 | Low | Low | Low | High | High | Low | Low | Low | Low | |
| Langendijk (2021)‐1 | Low | Low | Low | Low | Low | Low | Low | Low | Low | ||
| Langendijk (2021)‐2 | Low | Low | Low | Low | Low | Low | Low | Low | Low | ||
Applicability concerns
The developmental study, as well as two validation studies, were judged as having low concerns about applicability in all domains (Table).
Cavallo (2021)‐1 (xerostomia)
Description
The Cavallo (2021)‐1 model was developed for predicting acute xerostomia during radiotherapy for patients with 132 nasopharyngeal cancer patients at a European centre (Cavallo 2021). The model that was developed using LASSO includes four predictors: D98% in combined parotid glands, equivalent uniform dose in oral cavity, age, and smoking history (Table). The outcome was defined as grade 2 or 3 xerostomia based on CTCAE version 4.0. The model was presented as a nomogram. The model’s apparent performance showed a c‐statistic of 0.71 with good calibration performance. At internal validation, the model showed a c‐statistic of 0.67, with a calibration slope of 0.78 and a calibration‐in‐the‐large of 0.16, indicating overfitting of the model in the developmental cohort (Table).
9. Characteristics of included studies for Cavallo (2021)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Cavallo (2021)‐1 | Development | Cavallo (2021)‐1 | 2004‐2015 | 1, Europe | Retrospective | Consecutive | 90/132 | Median: 49, range: 18‐81 | Male: 70% | Nasopharynx: 100% | II: 15% III: 29% IV: 56% | IMRT: 53%, VMAT: 47% | Conventional fractionation | Concurrent: 100% induction: 77% |
| Validation | Cavallo (2021)‐1 | 2017‐2018 | 1, Europe | Prospective | Unclear | 34/38 | Median: 52, range: 24‐72 | Male: 79% | Nasopharynx: 100% | II: 16% III: 32% IV: 53% | VMAT: 100% | Conventional fractionation | Concurrent: 60% induction: 42% | |
| Cavallo (2021)‐2 | 2017‐2018 | 1, Europe | Prospective | Unclear | 77/93 | Median: 62, range: 23‐83 | Male: 73% | NR | II: 9% III: 30% IV: 62% | VMAT: 100% | Conventional fractionation | Concurrent: 60% induction: 12% | ||
Abbreviations: IMRT:intensity modulated radiotherapy RT: radiotherapy VMAT: volumetric modulated arc therapy
10. Model performance for Cavallo (2021)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Cavallo (2021)‐1 | Development | Cavallo (2021)‐1 | 0.71 | Figure 1 | NR | NA | NA | NA | NA |
| Validation | Cavallo (2021)‐1 | 0.73 | Figure 6A | Calibration slope: 0.77, calibration‐in‐the‐large 0.38, NPV 0.65, PPV 0.75 (at a cut‐off of 65%) | No update | NA | NA | NA | |
| Cavallo (2021)‐2 | 0.68 | Figure 6B | Calibration slope: 0.50 calibration‐in‐the‐large: 0.53, NPV: 0.52, PPV: 0.69 (at a cut‐off of 65%) | No update | NA | NA | NA | ||
Abbreviations: NA: not applicable NPV: negative predictive value NR: not reported PPV: positive predictive value
External validation
The model was validated in the same paper using two different types of cohorts: one including only nasopharyngeal cancer patients and the other including various types of head and neck cancer patients. The detailed information about recruiting methods for the validation cohorts was not provided. The model showed c‐statistics of 0.73 and 0.68 in the cohort of nasopharyngeal cancer patients and the other cohort of head and neck cancer patients, respectively. The calibration plots showed poor performance in both cohorts (Table).
Risk of bias
The model development as well as the two validations were judged as having high ROB in the analysis domain, mainly due to the small sample size. Both validations were judged as being unclear in the participants' domain as no detailed information about recruiting was provided (Table).
11. Risk of bias assessment (PROBAST) of Cavallo (2021)‐1.
| Model | Study | PROBAST ‐ Risk of bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Cavallo (2021)‐1 | Development | Cavallo (2021)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low |
| Validation | Cavallo (2021)‐1 | Unclear | Low | Low | High | High | Low | Low | Low | Low | |
| Cavallo (2021)‐2 | Unclear | Low | Low | High | High | Low | Low | Low | Low | ||
Applicability
The developmental study, as well as two validation studies, were judged as having low concerns about applicability in all domains (Table).
Discrimination performance of the above models for xerostomia in external validation studies is summarised in Figure.
7.
Forest plot of models for xerostomia
Models with ≥ 2 external validations for swallowing‐related outcomes
Amongst 85 models developed for swallowing‐related outcomes, two models with ≥ two external validations were identified.
Christianen (2012)‐1 (dysphagia)
Description
The Christianen (2012)‐1 model predicts the presence of RTOG/EORTC grade 2‐4 dysphagia six months after radiotherapy, and was developed using prospectively collected data of a consecutive cohort of 354 individuals who received curatively intended IMRT or 3D‐conformal radiation therapy with or without concomitant chemotherapy from 1997 at the VU University Medical Centre or the University Medical Centre Groningen, both in the Netherlands (Christianen 2012) (Table). The final logistic regression model included two predictors: mean dose to the superior pharyngeal constrictor muscle, and mean dose to the supraglottic larynx. They reported an apparent c‐statistic in the development data of 0.80 (95% CI 0.75 to 0.85), a P value in an HL test of 0.19, a scaled Brier score of 0.23 (95% CI 0.15 to 0.31), an R2 of 0.31 (95% CI 0.21 to 0.41), and a discrimination slope of 0.22 (95% CI 0.18 to 0.25). No optimism‐corrected internal validation results were reported (Table).
12. Characteristics of included studies for Christianen (2012)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Christianen (2012)‐1 | Development | Christianen (2012)‐1 | 1997‐NR | 2, Europe | Prospective | Yes | NR/354 | > 65 years: 37% | Male: 74% | Oral cavity: 5%, larynx: 47%, hypopharynx: 5%, oropharynx: 26%, nasopharynx: 4%, others: 13% | NR | IMRT: 38% 3D‐conformal: 62% | 70 Gy conventional and accelerated | Concurrent: 20% |
| Validation | Blanchard (2016)‐2 | 2011‐2015 | 1, North America | Prospective | Unclear | 27/89 | Median: 60, IQR: 19‐92 | Male: 70% | Hypopharynx: 3%, oropharynx: 45%, nasopharynx: 14% | NR | Protons | NR | Concurrent: 56%, 24% inductive 10% molecular | |
| Christianen (2016)‐1 | 2010‐2014 | 2, Europe | Prospective | Yes | 42/186 | Mean: 64 | Male: 74% | Oral cavity: 4%, larynx: 46%, hypopharynx: 11%, oropharynx: 35%, nasopharynx: 4%, others: 0% | NR | IMRT | Conventional and accelerated | Concurrent: 33%, molecular: 6% | ||
| Hansen (2019)‐1 | 2007‐2012 | 6, Europe | Randomised trial participants | Unclear | 94/284 | NR | NR | NR | NR | NR | NR | NR | ||
| Huynh (2023)‐1 | 2018‐2020 | 1, Europe | Cross‐sectional | No | 75/239 | Mean: 56 | Male: 67% | Oral cavity: 17%, larynx: 6%, hypopharynx: 2%, oropharynx: 53%, nasopharynx: 3%, others: 19% | NR | IMRT: 55% 3D‐conformal: 45% | 50‐70 Gy conventional | Concurrent: 52% | ||
| Langendijk (2021)‐1 | NR | 1, Europe | Prospective | Yes | 109/354 | > 65 years: 37% | Male: 71% | Oral cavity: 9%, larynx: 39%, oropharynx: 42%, nasopharynx: 3%, others: 7% | NR | NR | Conventional and accelerated | NR | ||
| Langendijk (2021)‐2 | NR | 1, Europe | Prospective | Yes | 242/813 | > 65 years: 38% | Male: 72% | Oral cavity: 6%, larynx: 42%, oropharynx: 33%, nasopharynx: 4%, others: 15% | NR | NR | Conventional and accelerated | NR | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range NR: not reported RT: radiotherapy
13. Model performance for Christianen (2012)‐1.
| Model | Study | Original | Update | ||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Christianen (2012)‐1 | Development | Christianen (2012)‐1 | 0.80 (0.75 to 0.85) | NR | HL test P = 0.19 Scaled Brier 0.23 (0.15 to 0.31) R2 0.31 (0.21 to 0.41) Discrimination slope 0.22 (0.18 to 0.25) | NA | NA | NA | NA |
| Validation | Blanchard (2016)‐2 | 0.71 (0.59 to 0.82) | NR | HL test P = 0.23 | Complete model revision | NR | NR | HL test P = 0.66 | |
| Christianen (2016)‐1 | 0.75 (0.68 to 0.82) | NR | HL test P = 0.74, scaled Brier 0.13 (0.03 to 0.22), R2 0.21 (0.08 to 0.33), discrimination slope 0.14 (0.1 to 0.18) | No update | NA | NA | NA | ||
| Hansen (2019)‐1 | 0.68 | Figure 1 | Brier 0.47 | Recalibration‐in‐the‐large | NR | Figure 1 | Brier 0.42 | ||
| Huynh (2023)‐1 | 0.66 | Figure 3 | Brier 0.15 | Recalibration‐in‐the‐large | 0.72 | Figure 3 | Brier 0.15 | ||
| Langendijk (2021)‐1 | 0.74 | Figure S3 | Calibration‐in‐the‐large 0.146, calibration slope 0.772 | Complete model revision | 0.77 | Figure S3 | NR | ||
| Langendijk (2021)‐2 | NA | NA | NA | Complete model revision | 0.81 | Figure S3 | Calibration‐in‐the‐large 0.12, calibration slope 1.2 | ||
Abbreviations: HL: Hosmer‐Lemeshow NA: not applicable NR: not reported
External validation
The Christianen (2012)‐1 model was externally validated in five studies (Blanchard 2016; Christianen 2016; Hansen 2019; Huynh 2023; Langendijk 2021). Blanchard 2016 validated the model in a prospective North American single centre cohort of 89 patients, resulting in a c‐statistic of 0.71 (95% CI 0.59 to 0.82) and a P value in an HL test of 0.23. They also performed a complete model revision by re‐estimating all coefficients, resulting in a P value in an HL test of 0.66. The most notable difference with Christianen 2012 is that patients were treated with proton therapy in Blanchard 2016 instead of IMRT and 3D‐conformal radiotherapy. Christianen 2016 validated their own model in a more recent (2010‐2014) prospective two‐centre European cohort of 186 patients receiving IMRT but, in other aspects, quite similar to the development cohort. They reported a c‐statistic of 0.75 (95% CI 0.68 to 0.82), a P value in an HL test of 0.74, a scaled Brier of 0.13 (95% CI 0.03 to 0.22), an R2 of 0.21 (95% CI 0.08 to 0.33), and a discrimination slope of 0.14 (95% CI 0.1 to 0.18). No model update was performed. Hansen 2019 validated the model in data of 284 patients from a six‐centre European randomised controlled trial, reporting a c‐statistic of 0.68 and a Brier score of 0.47 without model update. The Brier score was 0.42 when the model was recalibrated by intercept refitting. An important difference lies in the study population: Christianen 2012 used participants from a consecutive cohort, while Hansen 2019 used participants from a randomised controlled trial. Huynh 2023 validated the model in a cross‐sectional survey in a European centre cohort of 239 head and neck cancer patients. The model showed a c‐statistic of 0.66, which improved to 0.72 when it was recalibrated by intercept refitting. The timing of the outcome assessment was not explained in detail. Langendijk 2021, the same author group as the original developmental study, validated the model using cohorts including patients quite similar to the original developmental study. Subsequently, they updated the model using the data combining the validation cohort with the original developmental cohort. The original model showed a c‐statistic of 0.74, which improved to 0.81 with good calibration performance by a complete model revision (Table).
Based on the five validations, we performed meta‐analyses to pool c‐statistics. The pooled c‐statistic was 0.71 (95% CI 0.65 to 0.76, 95% prediction interval 0.60 to 0.79) with an I2 of 29%. As indicated by the narrow prediction interval and low I2, the discriminative performance of the Christianen 2012 model was expected to be consistent.
Risk of bias
The development study was rated as having low ROB in all domains but the analysis domain, which was judged as unclear, due to an unclear number of participants with the outcome, and lack of clarity on how missing data were handled (Christianen 2012) (Table). Apart from Hansen 2019 and Huynh 2023, all validation studies were given a low ROB in all domains except the analysis domain (Blanchard 2016; Christianen 2016; Langendijk 2021). Hansen 2019 had an unclear ROB for the participants' domain, due to the limited information on eligibility criteria. Huynh 2023 had high ROB in the participants and outcome domains because of its survey design. All validation studies except Langendijk 2021 were judged to have a high ROB as a result of their small sample size (Blanchard 2016; Christianen 2016; Hansen 2019).
14. Risk of bias assessment (PROBAST) of Christianen (2012)‐1.
| Model | Study | PROBAST ‐ Risk of Bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Christianen (2012)‐1 | Development | Christianen (2012)‐1 | Low | Low | Low | Unclear | Unclear | Low | Low | Low | Low |
| Validation | Blanchard (2016)‐2 | Low | Low | Low | High | High | Low | Low | Low | Low | |
| Christianen (2016)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low | ||
| Hansen (2019)‐1 | Unclear | Low | Low | High | High | Unclear | Low | Low | Unclear | ||
| Huynh (2023)‐1 | High | Low | High | High | High | Low | Low | Low | Low | ||
| Langendijk (2021)‐1 | Low | Low | Low | Low | Low | Low | Low | Low | Low | ||
| Langendijk (2021)‐2 | Low | Low | Low | Low | Low | Low | Low | Low | Low | ||
Applicability concerns
For the model development study, concerns about applicability were low (Christianen 2012). The validation studies by Blanchard 2016, Christianen 2016, Huynh 2023 and Langendijk 2021 were also judged to have low concerns about applicability in all domains. For Hansen 2019, concerns about applicability were low for the predictor and outcome domains, but unclear in the participant domain due to lack of clarity about participant eligibility (Table).
Wopken (2014b)‐1 (tube feeding dependence)
Description
The Wopken (2014b)‐1 model predicts tube feeding dependence six months after completion of radiotherapy. The model was developed from prospectively collected multicentre data of 355 patients receiving either IMRT or 3D‐conformal radiotherapy, with possibly concurrent chemotherapy (Wopken 2014b) (Table). The logistic model includes 10 predictors: advanced T‐stage, presence of moderate weight loss, presence of severe weight loss, use of accelerated radiotherapy, chemoradiation, radiotherapy plus cetuximab, mean dose to the superior pharyngeal constrictor muscle, mean dose to the inferior pharyngeal constrictor muscle, mean dose to the contralateral parotid, and mean dose to the cricopharyngeal muscle. The authors reported an apparent c‐statistic of 0.88 and an internal cross‐validated c‐statistic of 0.85, a calibration slope of 0.27, and a Nagelkerke R2 of 0.4 (Table).
15. Characteristics of included studies for Wopken (2014b)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Wopken (2014b) ‐1 | Development | Wopken (2014b)‐1 | NR | 2, Europe | Prospective | Yes | 38/355 | Mean age: 62, > 65: 36% | Male: 76% | Oral cavity: 5%, larynx: 53%, hypopharynx: 8%, oropharynx: 28%, nasopharynx: 5% others: 1% | NR | IMRT: 49% 3D‐conformal: 51% | 50‐70 Gy accelerated and conventional | Concurrent: 25% |
| Validation | Blanchard (2016)‐1 | 2011‐2015 | 1, North America | Prospective | Unclear | 4/89 | Median: 60, IQR: 19‐92 | Male: 70% | Hypopharynx: 3%, oropharynx: 45%, nasopharynx: 14% | NR | Proton: 100% | NR | Concurrent: 56%, induction: 24%, molecular: 10% | |
| Kanayama (2018)‐1 | 2009‐2013 | 1, Asia | Unclear | Unclear | 7/122 | Median: 63 range: 20‐89 | Male: 82% | Larynx: 6%, hypopharynx: 16%, oropharynx: 43%, nasopharynx: 34%, others: 2% | NR | NR | 70 Gy Conventional | Concurrent: 83% | ||
| Langendijk (2021)‐5 | NR | 1, Europe | Prospective | Yes | 55/457 | > 65 years: 39% | Male: 72% | Oral cavity: 8%, larynx: 39%, oropharynx 39%, nasopharynx: 4%, others: 18% | NR | NR | NR | Concurrent: 32% | ||
| Langendijk (2021)‐6 | NR | 1, Europe | Prospective | Yes | 93/812 | > 65 years: 38% | Male: 73% | Oral cavity: 7%, larynx: 45%, oropharynx 35%, nasopharynx: 4%, others: 9% | NR | NR | NR | Concurrent: 32% | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range NR: not reported RT: radiotherapy
16. Model performance for Wopken (2014b)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Wopken (2014b)‐1 | Development | Wopken (2014b)‐1 | 0.88 | Calibration slope 0.27 | Nagelkerke R2 0.4 | NA | NA | NA | NA |
| Validation | Blanchard (2016)‐1 | 0.95 (0.85 to 1.00) | NR | HL test P = 0.43 | No update | NR | NR | NR | |
| Kanayama (2018)‐1 | 0.79 (0.65 to 0.90) | NR | Discrimination slope: 0.18 (0.08 to 0.24), HL test P = 0.38 R2: 0.05 (‐0.01 to 0.38) calibration‐in‐the‐large ‐0.99, calibration slope 0.97 | Recalibration‐in‐the‐large | NR | NR | LR 0.03 compared to the original model | ||
| Langendijk (2021)‐5 | 0.85 | Figure S4 | Calibration‐in‐the‐large: 0.42, calibration slope: 0.81 | Complete model revision | 0.86 | Figure S4 | Calibration‐in‐the‐large: ‐0.001, calibration slope: 0.992 | ||
| Langendijk (2021)‐6 | NA | NA | NA | Complete model revision | 0.85 | Figure S4 | Calibration‐in‐the‐large: 0.110, calibration slope: 1.080 | ||
Abbreviations: HL: Hosmer‐Lemeshow LR: likelihood ratio test NA: not applicable NR: not reported
External validation
The developed model was externally validated in three other studies (Blanchard 2016; Kanayama 2018; Langendijk 2021). Blanchard 2016 validated the model in prospectively collected data of 89 patients that received proton therapy, with possibly concurrent chemotherapy, from 2011‐2015 at a single North American centre, reporting a c‐statistic of 0.95 (95% CI 0.85 to 1.00), and a P value in an HL test of 0.43. No model update was performed. Kanayama 2018 validated the model in data of 122 patients recruited from 2009‐2013 at a single centre in Asia, reporting a c‐statistic of 0.79 (95% CI 0.65 to 0.90), a calibration‐in‐the‐large of ‐0.99 and calibration slope of 0.97, a discrimination slope of 0.18 (95% CI 0.08 to 0.24), a P value in an HL test of 0.38, and an R2 of 0.05 (95% CI ‐0.01 to 0.38). They also reported a P value of 0.03 in the likelihood ratio test, compared with the original model, when the model was updated by intercept refitting (Table). Langendijk 2021, the same author group as the original developmental study, validated the model using cohorts including patients quite similar to the original developmental study. Although the original model showed a c‐statistic of 0.85, it showed under prediction in calibration performance. Therefore, they completely revised the model using the data combining the validation cohort with the original developmental cohort. The updated model showed a c‐statistic of 0.85 with better calibration performance.
Risk of bias
For the development study (Wopken 2014b), as well as the validation studies (Blanchard 2016; Kanayama 2018; Langendijk 2021), the judged ROB was high for the analysis domain (Table). The primary reason for this was the small number of outcome events in the development data, but also in the validation datasets used.
17. Risk of bias assessment (PROBAST) of Wopken (2014b)‐1.
| Model | Study | PROBAST ‐ Risk of bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Wopken (2014b)‐1 | Development | Wopken (2014b)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low |
| Validation | Blanchard (2016)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low | |
| Kanayama (2018)‐1 | Unclear | Low | Low | High | High | Low | Low | Low | Low | ||
| Langendijk (2021)‐5 | Low | Low | Low | High | High | Low | Low | Low | Low | ||
| Langendijk (2021)‐6 | Low | Low | Low | High | High | Low | Low | Low | Low | ||
Applicability concerns
For the model development study (Wopken 2014b), as well as the validation studies (Blanchard 2016; Kanayama 2018; Langendijk 2021), concerns about applicability were low in all domains (Table).
Discrimination performance of those models for swallowing‐related outcomes in external validation studies is summarised in Figure.
8.
Forest plot of models for swallowing‐related outcomes
Models with ≥ 2 external validations for hypothyroidism
Amongst 72 developed hypothyroidism models, three models with ≥ two external validations were identified.
Boomsma (2012)‐1 (clinical or subclinical hypothyroidism)
Description
The Boomsma (2012)‐1 model predicts clinical or subclinical hypothyroidism within two years after radiotherapy and was developed using prospectively collected data of a consecutive cohort of 105 individuals who started primary or postoperative IMRT or 3D‐conformal radiation therapy with or without concomitant chemotherapy in 2007 or 2008 at the University Medical Centre Groningen (The Netherlands) (Table). The mean follow‐up time was 2.5 years. The final logistic regression model includes two predictors: mean dose to the thyroid gland, and the thyroid gland volume. The reported apparent c‐statistic in the development data was 0.85 (95% CI 0.78 to 0.92). Internal validation was not performed (Table).
18. Characteristics of included studies for Boomsma (2012)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No of center, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Boomsma (2012)‐1 | Development | Boomsma (2012)‐1 | 2007‐2008 | 1, Europe | Prospective | Yes | 35/105 | 18‐49 y: 16%, 50‐59 y: 25%, 60‐69 y: 31%, ≥ 70 y: 29% | Male: 27% | Oral cavity: 14%, larynx: 32%, hypopharynx: 9%, oropharynx: 28%, others: 17% | NR | 33% IMRT 3D‐conformal: 67% | Conventional: 48% accelerated: 52% | Concurrent: 14% |
| Validation | Blanchard (2016)‐4 | 2011‐2015 | 1, North America | Prospective | Unclear | 40/58 | Median: 60, IQR: 19‐92 | Male: 70% | Hypopharynx: 3%, oropharynx: 45%, nasopharynx: 14% | NR | Protons | NR | Concurrent: 56%, induction: 24%, molecular: 10% | |
| Luo (2018)‐1 | 2007‐2015 | 1, Asia | Prospective | Yes | 39/174 | Median: 50, range: 16‐69 | Male: 74% | Nasopharynx: 100% | I‐II: 12%, III‐IV: 89% | IMRT: 82% 3D‐conformal: 18% | 70 Gy conventional | NR | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range NA: not applicable NR: not reported RT: radiotherapy
19. Model performance for Boomsma (2012)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Boomsma (2012)‐1 | Development | Boomsma (2012) | 0.85 (0.78 to 0.92) | NR | NR | NA | NA | NA | NA |
| Validation | Blanchard‐4 | 0.74 (0.57 to 0.91) | NR | HL test P = 0.01 | Complete model revision | NR | NR | HL test P = 0.53 | |
| Luo‐1 | 0.64 (0.56 to 0.71) | NR | NR | No update | NA | NA | NA | ||
Abbreviations: HL: Hosmer‐Lemeshow NA: not applicable NR: not reported
External validation
The original Boomsma (2012)‐1 model was externally validated in two separate studies (Blanchard 2016; Luo 2018) for the same outcome as its developmental study. Both validation studies were conducted in patient groups predominantly consisting of males (at least 70%), whereas Boomsma 2012 was developed on a predominantly female patient group (73%). Blanchard 2016 validated the model in a prospective North American single centre cohort of 58 patients, resulting in a c‐statistic of 0.74 (95% CI 0.57 to 0.91) and a P value in an HL test of 0.01. The P value of the HL test was 0.53 when all the coefficients were re‐estimated. A notable difference with Boomsma 2012 is the use of proton therapy in Blanchard 2016 instead of IMRT and 3D‐conformal radiation therapy. Luo 2018 validated the model in data of 174 patients, prospectively collected from a single Asian centre, resulting in a c‐statistic of 0.64 (95% CI 0.56 to 0.71). No model update was performed. Notable differences with Boomsma 2012 are the recruitment region, but also that Luo 2018 included only patients with nasopharynx cancer.
Risk of bias
We rated the ROB for the development study as low across all domains, except for the analysis domain, which was rated as high due to the relatively low number of participants, the selection of predictors based on univariate analysis, and not having accounted for optimism in model performance evaluation (Boomsma 2012). Both validation studies were given an overall high ROB. This was due to high ROB in the analysis domain as a result of a small sample size (Table).
20. Risk of bias assessment (PROBAST) of Boomsma (2012)‐1.
| Model | Study | PROBAST ‐ Risk of bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Boomsma (2012)‐1 | Development | Boomsma (2012) | Low | Low | Low | High | High | Low | Low | Low | Low |
| Validation | Blanchard‐4 | Low | Low | Low | High | High | Low | Low | Low | Low | |
| Luo‐1 | Low | Low | Low | High | High | Low | Low | Low | Low | ||
Applicability concerns
The development study, as well as both validation studies, were judged to have low concerns about applicability in all domains (Table).
Ronjom (2013)‐1 (biochemical hypothyroidism)
Description
The Ronjom (2013)‐1 model predicts the presence of radiation‐induced biochemical hypothyroidism anytime after radiotherapy and was developed using retrospective data of a consecutive cohort of 203 patients who received IMRT from 2002 to 2010 at the Department of Oncology, Odense University Hospital (Denmark) and primary tumour located in the oral cavity, oropharynx, or the hypopharynx (Table). Their final logistic regression model includes two predictors: the thyroid gland volume, and the mean dose to the thyroid gland. They did not report information about their model’s performance (Table).
21. Characteristics of included studies for Ronjom (2013)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Ronjom (2013)‐1 | Development | Ronjom (2013)‐1 | 2002‐2010 | 1, Europe | Retrospective | Yes | 35 / 203 | Median: 61, range: 42‐87 | Male: 77% | Oral cavity: 4%, larynx: 41%, hypopharynx: 5%, oropharynx: 50% | I‐II: 41%, III‐IV: 59% | IMRT | 66‐68 Gy conventional and accelerated | Concurrent: 22% |
| Validation | Ronjom (2015)‐1 | 2012 | 1, Europe | Retrospective | Yes | 38/198 | Median: 60, range: 31‐85 | Male: 78% | Oral cavity: 3%, larynx: 41%, hypopharynx: 5%, oropharynx: 51% | NR | NR | 66‐68 Gy | Concurrent: 39% | |
| Luo (2018)‐4 | 2007‐2015 | 1, Asia | Prospective | Yes | 39/174 | Median: 50, IQR: 16‐69 | Male: 74% | Nasopharynx: 100% | I‐II: 12%, III‐IV: 89% | IMRT: 82% 3D‐conformal: 18% | 70 Gy | NR | ||
| Zhu (2021)‐4 | 2012‐2015 | 1, Asia | Retrospective | Unclear | 138/244 | Median: 43, range: 11‐79 for those with the outcome Median: 48, range: 20‐81 for those without the outcome | Male: 71% | Nasopharynx: 100% | I: 3% II: 17% III: 60% IV: 20% | IMRT: 100% | 66‐72 Gy Conventional | Concurrent: 82% | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range NA: not applicable NR: not reported RT: radiotherapy
22. Model performance for Ronjom (2013)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Ronjom (2013)‐1 | Development | Ronjom (2013)‐1 | NR | NR | NR | NA | NA | NA | NA |
| Validation | Ronjom (2015)‐1 | NR | Calibration curve | Pearson cc 0.97 | No update | NA | NA | NA | |
| Luo (2018)‐4 | 0.65 (0.57 to 0.72) | NR | NR | No update | NA | NA | NA | ||
| Zhu (2021)‐4 | 0.69 (0.63 to 0.75) | NR | NR | No update | NA | NA | NA | ||
Abbreviations: cc: correlation coefficient NA: not applicable NR: not reported
External validation
The Ronjom (2013)‐1 model was externally validated in three studies (Luo 2018; Ronjom 2015; Zhu 2021). The same authors validated the model in more recently retrospectively collected data from 198 patients, reporting a calibration curve indicating acceptable calibration performance and a Pearson correlation coefficient of 0.97 between model predictions and observed outcomes (Ronjom 2015). Luo 2018 validated the model in prospectively collected data from 174 patients with nasopharyngeal cancer from an Asian centre that received IMRT or 3D‐conformal radiation therapy, reporting a c‐statistic of 0.65 (95% CI 0.57 to 0.72). Zhu 2021 validated the model in a retrospective cohort of 244 patients with nasopharyngeal cancer at an Asian centre that received IMRT. The model showed a c‐statistic of 0.69 (95% CI 0.63 to 0.75). Important to mention is that this validation was done in patients with the nasopharynx as primary tumour location, while these were explicitly excluded during model development. None of the two studies performed any model updates (Table).
Risk of bias
The development study (Ronjom 2013) was assessed to have low ROB in all domains but the analysis domain, which was rated as having high ROB due to the small sample size. The validation studies were rated as having high (Luo 2018; Ronjom 2015) and unclear ROB (Zhu 2021) in the analysis domain. In all other domains, they are rated as having low ROB (Table).
23. Risk of bias assessment (PROBAST) of Ronjom (2013)‐1.
| Model | Study | PROBAST ‐ Risk of Bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Ronjom (2013): Ronjom‐1 model | Development | Ronjom (2013)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low |
| Validation | Ronjom (2015)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low | |
| Luo (2018)‐4 | Low | Low | Low | High | High | Low | Low | Low | Low | ||
| Zhu (2021)‐4 | Low | Low | Low | Unclear | Unclear | Low | Low | Low | Low | ||
Applicability concerns
For the model development study (Ronjom 2013), as well as the validation studies (Luo 2018; Ronjom 2015; Zhu 2021), concerns about applicability were low in all domains (Table).
Cella (2012)‐2 (hypothyroidism)
Description
The Cella (2012)‐2 model predicts the presence of radiation‐induced clinical or subclinical hypothyroidism and was developed using retrospectively collected data of a consecutive cohort of 53 individuals with Hodgkin's lymphoma who received post‐chemotherapy supradiaphragmatic involved‐field radiation therapy at the Radiation Oncology Department of the University of Naples Federico II (Cella 2012) (Table). The model was not included in the developed models as it was originally developed in patients with supradiaphragmatic Hodgkin’s lymphoma, which also included axillary and mediastinal regions in addition to the head and neck region, and was consequently considered outside the scope of this review. The final logistic regression model includes three predictors: the thyroid V30 dose parameter [cc], the thyroid volume, and patient sex. They reported a c‐statistic of 0.87 (95% CI 0.75 to 0.95). Calibration was not reported (Table).
24. Characteristics of included studies for Cella (2012)‐2.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Cella (2012)‐2 | Development | Cella (2012)‐2 | NR | 1, Europe | Retrospective | Yes | 22/53 | Median: 28, range: 14‐70 | Male: 47% | Lymphoma: 100% | I‐II: 79%, III‐IV: 21% | 3D‐conformal | 32 Gy | Sequential: 100% |
| Validation | Luo (2018)‐3 | 2007‐2015 | 1, Asia | Prospective | Yes | 39/174 | Median: 50 IQR: 16 to 69 | Male: 74% | Nasopharynx: 100% | I‐II: 12% III‐IV: 89% | IMRT: 82% 3D‐conformal: 18% | 70Gy | NR | |
| Zhu (2021)‐2 | 2012‐2015 | 1, Asia | Retrospective | Unclear | 138/244 | Median: 43, range: 11‐79 for those with the outcome Median: 48, range: 20‐81 for those without the outcome | Male: 71% | Nasopharynx: 100% | I: 3% II: 17% III: 60% IV: 20% | IMRT: 100% | 66‐72 Gy conventional | Concurrent: 82% | ||
Abbreviations: IMRT: intensity modulated radiation therapy NA: not applicable NR: not reported RT: radiotherapy
25. Model performance for Cella (2012)‐2.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Cella (2012)‐2 | Development | Cella (2012)‐2 | 0.87 (0.75 to 0.95) | NR | NR | NA | NA | NA | NA |
| Validation | Luo (2018)‐3 | 0.68 (0.60 to 0.75) | NR | NR | No Update | NA | NA | NA | |
| Zhu (2021)‐2 | 0.65 (0.59 to 0.71) | NR | NR | No Update | NA | NA | NA | ||
Abbreviations: NA: not applicable NR: not reported
External validation
The Cella (2012)‐2 model was externally validated in two studies (Luo 2018; Zhu 2021). Luo 2018 validated the model in prospectively collected data of 174 patients with nasopharyngeal cancer, of which most were treated with IMRT, and some were treated with 3D‐conformal radiation therapy at a single centre in Asia, reporting a c‐statistic of 0.68 (95% CI 0.60 to 0.75) (Table). No model update was performed in this study. Zhu 2021 validated the model in a retrospective cohort of 244 patients with nasopharyngeal cancer at an Asian centre that received IMRT. The model showed a c‐statistic of 0.65 (95% CI 0.59 to 0.71). No calibration performance was reported. Cella 2012 developed their model in Hodgkin’s lymphoma patients, while Luo 2018 and Zhu 2021 validated the model in nasopharynx patients. Additionally, both validation studies were conducted on data from patients of which the majority received IMRT, while the model was developed in patients treated with 3D‐conformal radiation therapy.
Risk of bias
The development study was rated as having high ROB in all domains but the predictor's domain, which was judged as having low ROB (Cella 2012). The high ROB in the participants, outcome and analysis domains was given due to having an estimated performance in case‐control study data, the absence of a clear timing of the predicted outcome, the small sample size, and the fact that complete case analysis was used to handle missing data. Luo 2018 had high ROB in the analysis domain, as a result of their small validation sample size (Table). Zhu 2021 was judged as being unclear in the analysis domain.
26. Risk of bias assessment (PROBAST) of Cella (2012)‐2.
| Model | Study | PROBAST ‐ Risk of Bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall judgement | |||
| Cella (2012)‐2 model | Development | Cella (2012)‐2 | High | Low | High | High | High | Low | Low | Low | Low |
| Validation | Luo (2018)‐3 | Low | Low | Low | High | High | Low | Low | Low | Low | |
| Zhu (2021)‐2 | Low | Low | Low | Unclear | Unclear | Low | Low | Low | Low | ||
Applicability concerns
For the model development study, concerns about applicability were low (Cella 2012). Both validation studies were also judged to have low concern about applicability in all domains (Table).
Discrimination performance of those models for hypothyroidism in external validation studies is summarised in Figure.
9.
Forest plot of models for hypothyroidism
Models with ≥ 2 external validations for brain and nerve‐related outcomes
Amongst 40 models for brain and nerve‐related outcomes, three models with ≥ two external validations for temporal lobe injury were identified.
OuYang (2023)‐1 (temporal lobe injury)
Description
The OuYang (2023)‐1 model was developed for predicting radiation‐induced temporal lobe injury in patients with nasopharyngeal cancer using a deep learning model (OuYang 2023). At four Asian centres, 6292 temporal lobes were retrospectively analysed (Table). The model, which included age, T stage, concurrent chemotherapy, and 18 dose‐volume histogram parameters, showed a c‐statistic of 0.79 (95% CI 0.77 to 0.81) with good calibration (Table).
27. Characteristics of included studies for OuYang (2023)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No centres, region | Design | Consecutive | N of events/N of patients | Age | Gender | Tumour location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| OuYang (2023)‐1 | Development | OuYang (2023)‐1 | 2014‐2018 | 4, Asia | Retrospective | Consecutive | 519/6292 temporal lobes | Median: 45 | Male: 71% | Nasopharynx: 100% | NR | IMRT: 100% | 66‐72 Gy conventional fractionation | Concurrent: 82% |
| Validation | OuYang (2023)‐1 | 2017 | 4, Asia | Prospective | Consecutive | 174/1930 temporal lobes | Median: 45, IQR: 37 to 53 | Male: 73% | Nasopharynx: 100% | NR | IMRT: 100% | 66‐72 Gy conventional fractionation | Concurrent: 87% | |
| OuYang (2023)‐2 | 2014‐2018 | 4, Asia | Retrospective | Consecutive | 164/2780 temporal lobes | NR | Male: 70% | Nasopharynx: 100% | NR | IMRT: 100% | 66‐72 Gy conventional fractionation | Concurrent: 79% | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range NA: not applicable NR: not reported RT: radiotherapy
28. Model performance for OuYang (2023)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| OuYang (2023)‐1 | Development | OuYang (2023)‐1 | 0.79 (0.77 to 0.81) | Supplementary Figure 3A | Time‐specific temporal lobe injury probabilities in Supplementary Figure 5A | NA | NA | NA | NA |
| Validation | OuYang (2023)‐1 | 0.82 (0.79 to 0.85) | Supplementary Figure 3C | Decision curve analysis in Supplementary Figure 4B, time‐specific temporal lobe injury probabilities in Supplementary Figure 5C | No update | NA | NA | NA | |
| OuYang (2023)‐2 | 0.80 (0.77 to 0.84) | Supplementary Figure 3D | Decision curve analysis in Supplementary Figure 4C, time‐specific temporal lobe injury probabilities in Supplementary Figure 5D | No update | NA | NA | NA | ||
Abbreviations: NA: not applicable
External validation
The OuYang (2023)‐1 model was validated in its original developmental study using two different cohorts: one with 1930 temporal lobes in a prospective cohort at an Asian centre and the other with 2780 temporal lobes in a retrospective cohort at three Asian centres (OuYang 2023). Both cohorts included only patients with nasopharyngeal cancer treated with IMRT. The model showed c‐statistics of 0.82 (0.79 to 0.85) and 0.80 (0.77 to 0.84), respectively. Calibration performance in both cohorts was not satisfactory compared to that in the developmental cohort (Table).
Risk of bias
The original developmental study was judged as having high ROB in the analysis domain, due to the univariable selection of predictors, and not taking into account the correlation between left and right temporal lobes within a certain individual. Both validations were judged as having low ROB in all domains (Table).
29. Risk of bias assessment (PROBAST) of OuYang (2023)‐1.
| Model | Study | PROBAST ‐ Risk of Bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall judgement | |||
| OuYang (2023)‐1 | Development | OuYang (2023)‐1 | Low | Low | Low | High | High | Low | Low | Low | Low |
| Validation | OuYang (2023)‐1 | Low | Low | Low | Low | Low | Low | Low | Low | Low | |
| OuYang (2023)‐2 | Low | Low | Low | Low | Low | Low | Low | Low | Low | ||
Applicability
The developmental process, as well as the two validations, were judged as having low concern about applicability in all domains (Table).
Wen (2021)‐1 (temporal lobe injury)
Description
The Wen (2021)‐1 model was developed for predicting temporal lobe injury in 8194 patients with newly diagnosed nasopharyngeal cancer treated with IMRT at an Asian centre (Wen 2021). The model was developed using Cox proportional hazards modelling, including age, T stage, and dose delivered to 0.5 cm3 temporal‐lobe volume (Table). A nomogram was presented for the model. The model showed a c‐statistic of 0.78 (95% CI 0.77 to 0.80) with good calibration (Table).
30. Characteristics of included studies for Wen (2021)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No of center, region | Design | Consecutive | N of events / N of patient | Age | Gender | Tumor location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Wen (2021)‐1 | Development | Wen (2021)‐1 | 2009‐2015 | 1, Asia | Retrospective | Consecutive | 989/8194 patients | <44 years: 43% in those with temporal lobe injury, 47% in those without temporal lobe injury | Male: 73% | Nasopharynx: 100% | I: 5% II: 17% III: 47% IV: 31% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 80% |
| Validation | OuYang (2023)‐3 | 2017 | 4, Asia | Prospective | Consecutive | 174/1930 temporal lobes | Median: 45 IQR: 37‐53 | Male: 73% | Nasopharynx: 100% | NR | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 87% | |
| OuYang (2023)‐4 | 2014‐2018 | 4, Asia | Retrospective | Consecutive | 164/2780 temporal lobes | NR | Male: 70% | Nasopharynx: 100% | NR | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 79% | ||
| Yang (2023a)‐1 | 2014‐2015 | 1, Asia | Retrospective | Unclear | 428/5006 temporal lobes | Median 44 (IQR 38‐51) in those with temporal lobe injury and median 44 (IQR 37‐52) in those without temporal lobe injury | Male: 70% | Nasopharynx: 100% | I: 2% II: 11% III: 52% IV: 36% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 81% | ||
| Yang (2023a)‐2 | 2014‐2015 | 1, Asia | Retrospective | Unclear | 163/2144 temporal lobes | Median 47 (IQR 38‐53) in those with temporal lobe injury and median 45 (IQR 38‐53) in those without temporal lobe injury | Male: 73% | Nasopharynx: 100% | I: 3% II: 9% III: 51% IV: 37% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 82% | ||
| Yang (2023a)‐3 | 2017‐NR | 1, Asia | Prospective | Unclear | 122/1976 temporal lobes | Median 47 (IQR 41‐54) in those with temporal lobe injury and median 45 (IQR 37‐54) in those without temporal lobe injury | Male: 73% | Nasopharynx: 100% | I: 2% II: 7% III: 54% IV: 37% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 86% | ||
| Yang (2023a)‐4 | 2014‐2015 | 1, Asia | Retrospective | Unclear | 132/2072 temporal lobes | Median 49 (IQR 43‐58) in those with temporal lobe injury and median 48 (IQR 40‐57) in those without temporal lobe injury | Male: 71% | Nasopharynx: 100% | I: 1% II: 7% III: 39% IV: 53% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 76% | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range NR: not reported RT: radiotherapy
31. Model performance for Wen (2021)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Wen (2021)‐1 | Development | Wen (2021)‐1 | 0.78 (0.77‐0.80) | Figure 2C | AUC at 5 year: 0.832 | NA | NA | NA | NA |
| Validation | OuYang (2023)‐3 | 0.77 (0.73‐0.80) | NR | NR | No update | NA | NA | NA | |
| OuYang (2023)‐4 | 0.79 (0.75‐0.82) | NR | NR | No update | NA | NA | NA | ||
| Yang (2023a)‐1 | 0.75 (0.73‐0.77) | NR | NR | No update | NA | NA | NA | ||
| Yang (2023a)‐2 | 0.78 (0.75‐0.81) | NR | NR | No update | NA | NA | NA | ||
| Yang (2023a)‐3 | 0.76 (0.71‐0.80) | NR | NR | No update | NA | NA | NA | ||
| Yang (2023a)‐4 | 0.75 (0.71‐0.79) | NR | NR | No update | NA | NA | NA | ||
Abbreviations: AUC: area under the curve HL: Hosmer‐Lemeshow NA: not applicable NR: not reported
External validation
The Wen (2021)‐1 model was externally validated in two studies using six cohorts (OuYang 2023; Yang 2023a). OuYang 2023 validated the model using two cohorts: one with 1930 temporal lobes from a prospective cohort at an Asian centre and the other with 2780 temporal lobes from a retrospective cohort at three Asian centres. Both cohorts included only patients with nasopharyngeal cancer treated with IMRT. The model showed c‐statistics of 0.77 (95% CI 0.73 to 0.80) and 0.79 (95% CI 0.75 to 0.82), respectively. Yang 2023aadditionally validated the Wen (2021)‐1 model in four cohorts of patients with nasopharyngeal cancer treated with IMRT: a training cohort of 5006 temporal lobes, an internal validation cohort of 2144 temporal lobes, a prospective test cohort of 1976 temporal lobes, and an external test cohort of 2072 temporal lobes. Compared with the original Wen 2021 development study, these cohorts were similar in tumour site, radiotherapy technique, and outcome concept, but included more recent multicentre, prospective, and external test data. The model showed c‐statistics of 0.75 (95% CI 0.73 to 0.77), 0.78 (95% CI 0.75 to 0.81), 0.76 (95% CI 0.71 to 0.80), and 0.75 (95% CI 0.71 to 0.79), respectively. Calibration performance was not reported in these validation cohorts. (Table)
Discrimination performance of those models for temporal lobe injury in external validation studies is summarised in Figure. Based on all six validations, the pooled c‐statistic was 0.77 (95% CI 0.75 to 0.78; 95% prediction interval 0.74 to 0.79; I² = 19%).
10.
Forest plot of models for temporal lobe injury
Risk of bias
The developmental study was judged as having low ROB in all domains. The two validations by OuYang 2023 were rated as having unclear ROB in the analysis domain. The four validations by Yang 2023awere rated as having high ROB in the analysis domain, mainly because the influence of missing data was unclear and calibration performance was not reported (Table).
32. Risk of bias assessment (PROBAST) of Wen (2021)‐1.
| Model | Study | PROBAST ‐ Risk of Bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Wen (2021)‐1 | Development | Wen (2021)‐1 | Low | Low | Low | Low | Low | Low | Low | Low | Low |
| Validation | OuYang (2023)‐3 | Low | Low | Low | Unclear | Unclear | Low | Low | Low | Low | |
| OuYang (2023)‐4 | Low | Low | Low | Unclear | Unclear | Low | Low | Low | Low | ||
| Yang (2023a)‐1 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear | ||
| Yang (2023a)‐2 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear | ||
| Yang (2023a)‐3 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear | ||
| Yang (2023a)‐4 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear | ||
Applicability
The developmental study and the two validations by OuYang 2023 were judged as having low concerns about applicability in all domains. For the four validations by Yang 2023a, concerns about applicability were low for predictors and outcome, but unclear for participants because the validation cohorts were derived from the same study population used to develop and test the Yang 2023a dosiomics model, and their eligibility and sampling process for validating the Wen model were not fully described separately (Table).
Yang (2023a)‐1 (temporal lobe injury)
Description
The Yang (2023a)‐1 model was developed for predicting temporal lobe injury‐free survival in patients with nasopharyngeal cancer treated with IMRT (Yang 2023a). The model was developed using a retrospective training cohort of 2503 patients, corresponding to 5006 temporal lobes (Table). The final Cox model included age, D1cc, and a dosiomics signature based on 10 dose‐distribution features, and was presented as a dosiomics nomogram. The model showed c‐statistics of 0.78 (95% CI 0.75 to 0.80) in the training cohort and 0.81 (95% CI 0.78 to 0.84) in the internal validation cohort, with calibration curves reported (Table).
33. Characteristics of included studies for Yang (2023a)‐1.
| Model | Study | Characteristics of included studies | ||||||||||||
| Recruitment | No of center, region | Design | Consecutive | N of events / N of patient | Age | Gender | Tumor location | Disease stage | RT technique | RT dose and fractionation | Chemotherapy | |||
| Yang (2023a)‐1 | Development | Yang (2023a)‐1 | 2014‐2015 | 1, Asia | Retrospective | Unclear | 428/5006 temporal lobes | Median 44 (IQR 38‐51) in those with temporal lobe injury and median 44 (IQR 37‐52) in those without temporal lobe injury | Male: 70% | Nasopharynx: 100% | I: 2% II: 11% III: 52% IV: 36% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 81% |
| Validation | Yang (2023a)‐5 | 2017 | 1, Asia | Prospective | Unclear | 122/1976 temporal lobes | Median 47 (IQR 41‐54) in those with temporal lobe injury and median 45 (IQR 37‐54) in those without temporal lobe injury | Male: 73% | Nasopharynx: 100% | I: 2% II: 7% III: 54% IV: 37% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 86% | |
| Yang (2023a)‐6 | 2014‐2015 | 1, Asia | Retrospective | Unclear | 132/2072 temporal lobes | Median 49 (IQR 43‐58) in those with temporal lobe injury and median 48 (IQR 40‐57) in those without temporal lobe injury | Male: 71% | Nasopharynx: 100% | I: 1% II: 7% III: 39% IV: 53% | IMRT: 100% | 66‐72 Gy Conventional fractionation | Concurrent: 76% | ||
Abbreviations: IMRT: intensity modulated radiation therapy IQR: interquartile range
RT: radiotherapy
34. Model performance for Yang (2023a)‐1.
| Model | Study | Characteristics of model performance | |||||||
| Original | Update | ||||||||
| C‐statistic | Calibration plot | Other | Technique | C‐statistic | Calibration plot | Other | |||
| Yang (2023a)‐1 | Development | Yang (2023a)‐1 | 0.78 (0.75‐0.80) | Figure 2B | Dosiomics nomogram in Figure 2A Decision curve analysis in Figure E5 | NA | NA | NA | NA |
| Validation | Yang (2023a)‐5 | 0.81 (0.77‐0.84) | Figure 2B | Decision curve analysis in Figure E5 | No update | NA | NA | NA | |
| Yang (2023a)‐6 | 0.79 (0.76‐0.83) | Figure 2B | Decision curve analysis in Figure E5 | No update | NA | NA | NA | ||
Abbreviations: NA: not applicable
External validation
The Yang (2023a)‐1 model was externally validated in the same study using two test cohorts: a prospective test cohort of 1976 temporal lobes and an external retrospective test cohort of 2072 temporal lobes. Both cohorts included patients with nasopharyngeal cancer treated with IMRT. The model showed c‐statistics of 0.81 (95% CI 0.77 to 0.84) and 0.79 (95% CI 0.76 to 0.83), respectively. Calibration curves were reported for both validation cohorts (Table). Compared with the development cohort, the prospective test cohort was more recent, while the external test cohort was recruited from a different Asian centre.
Risk of bias
The developmental study was judged as having high ROB in the analysis domain, mainly because information on handling of continuous predictors and missing data was limited. The two external validations were also rated as having high ROB in the analysis domain because the influence of missing data was unclear (Table).
35. Risk of bias (PROBAST) assessment of Yang (2023a)‐1.
| Model | Study | PROBAST ‐ Risk of Bias | PROBAST ‐ Applicability | ||||||||
| Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Domain 4: Analysis | Overall judgement | Domain 1: Participants | Domain 2: Predictors | Domain 3: Outcome | Overall Judgement | |||
| Yang (2023a)‐1 | Development | Yang (2023a)‐1 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear |
| Validation | Yang (2023a)‐5 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear | |
| Yang (2023a)‐6 | Low | Low | Low | High | High | Unclear | Low | Low | Unclear | ||
Applicability
Concerns about applicability were unclear for participants in both the developmental study and the two external validations, because selection was based on criteria such as regular MRI follow‐up and availability of radiotherapy plans, which may limit applicability to the broader target population. Concerns about applicability were low for predictors and outcome.(Table).
Models with 1 external validation or without any external validation studies
The individual characteristics of NTCP models with only one or no external validation are described in https://doi.org/10.17605/OSF.IO/MBHCG. The individual characteristics of external validations are given in https://doi.org/10.17605/OSF.IO/MBHCG.
Excluded studies
Of the 720 articles excluded at full‐text screening, we excluded 220 for reporting a conference abstract, nine for not presenting an original study, 348 for not aiming to develop or validate a prediction model, 64 for including the wrong study population, 18 for predicting a wrong outcome and 61 for other reasons (Figure). The articles and their reasons for exclusion are presented in the list of excluded studies (Characteristics of excluded studies).
Discussion
Summary of main results
In this review, our goal was to identify and describe all NTCP models of radiation‐induced toxicities in patients with head and neck cancer and assess the risk of bias and applicability concerns of these models. We identified 617 developed models from 152 articles. No external validation was identified for 481 models, while for the remaining 136 models and six additional models, 193 external validations were performed in 39 articles.
Model development studies
Of the 617 developed models identified in this review, 481 had no external validation, whereas 302 had neither external nor internal validation. Consequently, the predictive value of these models remains unknown in clinical settings other than the setting originally used for developing the models. Therefore, we do not recommend the use of those developed models without external validation in actual clinical settings. Amongst the developed models, the following models were judged as having low ROB and low concerns for applicability: Hansen for oral mucositis (Hansen 2020), Wen for temporal lobe injury (Wen 2021), Sheikh for xerostomia (Sheikh 2019), Wopken for tube feeding dependence (Wopken 2014a), Hamada for acute dermatitis (Hamada 2023), Van den Bosch for dysphagia, aspiration, xerostomia, sticky saliva, loss of taste, mucositis, hoarseness, speech problems, oral pain, throat pain, jaw pain, weight loss, nausea and vomiting, and fatigue (Van den Bosch 2021) and Van Rijn‐Dekker for xerostomia (Van Rijn‐Dekker 2023) . Because the models by Wopken, Van den Bosch and Van Rijn‐Dekker and their colleagues were validated once in the article which originally developed the models, further external validation by different authors is preferable.
Models with external validation
For salivary‐related outcomes, the models by Beetz (Beetz 2012b) and Cavallo (Cavallo 2021) were validated twice. The model by Beetz was developed to predict xerostomia in patients with head and neck cancer six months after radiotherapy. C‐statistics ranged from 0.70 to 0.74 in the two validations (Blanchard 2016; Langendijk 2021). Calibration performance was reported to be good in one study (Langendijk 2021). One validation study was rated as having low ROB in all domains (Langendijk 2021), while the other was rated as high ROB in the analysis domain (Blanchard 2016). The updated model in the validation by Langendijk and colleagues showed good discrimination and calibration performance, while it was assessed as having low ROB and low applicability concerns (Langendijk 2021). The other model by Cavallo 2021 predicts acute xerostomia during radiotherapy in patients with nasopharyngeal cancer (Cavallo 2021). The model was externally validated in the same study, using two different types of cohorts. C‐statistics ranged from 0.68 to 0.73 and calibration plots showed poor performance in both cohorts. Both validations were rated as having unclear ROB in the participants' domain because no detailed information about recruiting was provided.
The most frequently externally validated model for swallowing‐related outcomes was the model by Christianen and colleagues (Christianen 2012) with five external validations. It was developed to predict the occurrence of RTOG/EORTC grade 2–4 dysphagia six months after radiotherapy. The AUC values of the external validations of this model ranged from 0.66 to 0.75. Due to biases in analysis, the overall ROB of the original model development study was judged as unclear, while four external validations were assessed as having high overall ROB. Another model by Wopken and colleagues (Wopken 2014b) was validated three times. Both the model development study and external validations of these two models were assessed as having high ROB, except one external validation by Langendijk and colleagues (Langendijk 2021) Therefore, further evaluation is required for these two models for dysphagia before their clinical application.
The most frequently externally validated model for hypothyroidism was the model by Ronjom and colleagues (Ronjom 2013) with three external validations (Luo 2018; Ronjom 2015; Zhu 2021). It was developed to predict the occurrence of radiation‐induced biochemical hypothyroidism anytime after radiotherapy. The c‐statistics of the external validations of this model were not high, ranging from 0.65 to 0.69 (Luo 2018; Zhu 2021). Both development and external validation studies except one were assessed as having high ROB based on the PROBAST tool. In addition, two other models for hypothyroidism were identified (Boomsma 2012; Cella 2012), each of which had two external validations. None of those two models showed sufficient performance in studies with low ROB and applicability concerns. These results indicate that these three models for hypothyroidism should be used with great caution in clinical practice.
For temporal lobe injury in patients with nasopharyngeal cancer, the OuYang model predicts temporal lobe injury using a deep learning model in patients with nasopharyngeal cancer (OuYang 2023). The model was validated in the same paper using two different cohorts. C‐statistics ranged from 0.80 to 0.82, while calibration performance was not satisfactory. Another model by Wen (Wen 2021) was also validated by OuYang using the two cohorts (OuYang 2023) and by Yang using four cohorts (Yang 2023a). C‐statistics ranged from 0.75 to 0.79, while no calibration performance was reported. The model developed by Yang (Yang 2023a), using a dosiomics signature, D1cc, and age, was validated in the same paper using prospective and external test cohorts. C‐statistics ranged from 0.79 to 0.81, while calibration performance was assessed in both cohorts. Although the validations of the model by OuYang were rated as low in all ROB domains, the model formula to calculate an NTCP for an individual patient is not provided. The validations of the model by Wen were judged as unclear or high in the analysis domain. The validations of the model by Yang were judged as high ROB in the analysis domain.
Most of the other models validated only once were judged as having high ROB. Van den Bosch and colleagues developed 40 models and externally validated 36 of them in the same publication, but in patients from other centres, which were promising since all of them were assessed as having low ROB (Van den Bosch 2021). However, as these models were externally validated only once by the same authors, additional validations are recommended (preferably by a different author/group), before clinical implementation of these models.
Certainty of the evidence
Overall completeness of the data
Similar to the previous review by Sharabiani and colleagues (Sharabiani 2020), the reporting of predictive performance measures was poor in most studies. Of the 617 developed models, calibration and c‐statistics of the apparent performance were reported for 79 (13%) and 269 (44%), respectively. Furthermore, of the 315 models with internal validation, calibration after internal validation was reported only in 41 (13%) studies, while c‐statistics after internal validation were reported in 284 (90%) studies (Table).
Of the 193 external validations, validation was performed using the original model without any model update in 162 (84%), in which calibration was reported only in 40 (21%) and c‐statistics in 183 (95%). In 31 external validations, the model was updated after external validation, in which calibration and c‐statistics after model update were reported in 14 and 26 studies, respectively (Table).
We have summarised the missing information for ten models with at least two external validations in Appendix 4. The main missing information was calibration performance. In the original reference, calibration was not reported in three of ten model development studies and 15 of 32 external validations in total, while discrimination was not reported in one, respectively. To complete the data, we have sent emails to the four corresponding authors of the references, of which one responded at the time of writing.
In addition to model performance, patient recruitment method, events per variable, handling of missing data and number of patients with missing data for outcome and/or predictors, and radiation doses (especially in external validations) were the other most common missing components of the reporting. On the top of it, a model was developed but not presented in any form in 169 models (27%) in the article.
Certainty of the evidence
The grading of the summarised results of meta‐analysis of models was not performed since there is no official GRADE guidance available for rating the certainty of the evidence for prediction models.
Strengths and weaknesses of the review
Strengths of the review
This is an extensive review of NTCP models for any types of radiation‐induced side effects after radiotherapy in head and neck cancer patients. All relevant review processes were performed in duplicate and any conflicts were solved based on discussion amongst experts. Although the methodology of systematic reviews of NTCP model studies was still not well established at the time of conducting this review, we adjusted and adapted the latest knowledge and tools like PROBAST in close collaboration with the Cochrane Prognosis Methods group.
Potential bias in the review process
Screening
During the title and abstract and full‐text screening process, we often encountered articles in which the study aim was unclear. For instance, the aim was explicitly stated to find predictors for a certain outcome; however, a prediction model was suddenly presented in the results section without any description in the methods section. In such cases, we decided to include only studies in which the aim was clearly stated as to develop/validate a prediction model somewhere in the article. Despite a consensus decision following lively discussions between reviewers for each of such articles about whether to include or exclude them in the review, the judgement could sometimes still be subjective and, therefore, introduce risk of bias.
Risk of bias
We adapted CHARMS and PROBAST tools for ROB assessment. Due to the extensive number of references, a total of 12 reviewers were required. To make the judgement consistent, we performed pilot data extraction and discussed conflicts amongst all reviewers. However, some residual variability in scoring items amongst reviewers was unavoidable.
Applicability of findings to clinical practice and policy
Radiotherapy is the mainstay of treatment for head and neck cancer. When controlling the tumour, surrounding healthy tissues will also experience radiation exposure. The larger the exposure, the higher the risk of radiation‐induced toxicity. Therefore, the main strategy is to minimise radiation dose to these tissues without compromising tumour control. Historically, universal dose objectives were set for a limited set of normal tissues to prevent radiation‐induced toxicities. Although dose exposure to healthy tissues cannot be completely avoided, as these tissues may be located close to the tumour and because part of the dose is unavoidably shifted to other healthy tissues, a more comprehensive, multivariable strategy is needed to allow a more individualised approach (Rosenthal 2008a).Multivariable prediction or NTCP models describe the relationship between healthy tissue irradiation and the risk of radiation‐induced toxicities and have been increasingly integrated into daily clinical practice in recent years to reach an optimal balance between tumour control and toxicity prevention in individual patients (Kierkels 2014). These models reduce complication risks during treatment plan optimisation to actively guide the dose distribution (Kierkels 2014) and they facilitate the evaluation and decision between different treatment modalities, e.g. proton versus photon therapy, by comparing the predicted complication risk of each treatment plan (Langendijk 2013; Widder 2016). Treatment plan optimisation and so‐called model‐based selection require reliable and high‐quality NTCP models (Van den Bosch 2021). The current systematic review helps to identify relevant NTCP models that warrant further investigation, or even application in clinical practice, to eventually optimise individualised radiotherapy with respect to toxicity control.
Agreements and disagreements with other studies or reviews
We came across three previously conducted systematic reviews of NTCP models for head and neck cancer patients after radiotherapy (Brodin 2018; Sharabiani 2020; Stieb 2021). As indicated by the number of initially identified records in electronic databases and the final number of included model development and external model validation studies, our study is the most comprehensive amongst the three reviews. We identified more than 2000 records, and included 172 articles reporting on 617 developed models and 193 external validations. Brodin and colleagues focused on the validity of the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) dose constraints and variation in statistical methodology and reporting of various NTCP models. Their search was restricted to studies published after the publication of QUANTEC reports in 2010 and identified 343 references, of which 21 with quantitative dose‐response models were analysed (Brodin 2018). Sharabiani and colleagues focused on NTCP models without any restrictions on timing, identified more than 300 references, and included 52 studies, of which 51 reported on model development and one on external model validation. They have mainly focused on scoring original models according to TRIPOD type 1 to 4, i.e. whether the model had been externally validated and by whom (Sharabiani 2020). Stieb and colleagues specifically focused on NTCP models that predict late toxicities, identified 285 records, and included 56 studies, of which 48 reported on development and eight on the external validation of an NTCP model (Stieb 2021).
Similar to the aforementioned reviews, we found that the most frequently predicted outcomes were salivary‐ and swallowing‐related, that the number of included predictors was generally low, partly due to the inclusion of LKB models, and that internal and especially external validations of NTCP models were rare. Major disagreements were found related to the quality of the identified models, which is largely attributable to the fact that we used a formal ROB assessment tool (PROBAST), while the other three reviews did not. We found that, according to PROBAST, the vast majority of models were at high ROB, while Stieb and colleagues labelled several models as “good”(Stieb 2021) and Sharabiani and colleagues did not assess the quality of the models, but instead used the types of prediction model studies covered by TRIPOD (Collins 2015b; Moons 2015) to label model type (e.g. whether it had been externally validated or not; type 3/4) (Sharabiani 2020).
Authors' conclusions
Implications for practice
Amongst 617 developed models identified from 152articles, the overall ROB was high in 79%, mainly due to the issues observed in the analysis domain. Furthermore, approximately 80% of the models had never been externally validated, making their potential usefulness unknown in clinical settings. Amongst 136 models externally validated, we identified only ten models externally validated at least twice. Amongst them, there were two models to predict xerostomia (Beetz 2012b; Cavallo 2021), two models for swallowing‐related outcomes (Christianen 2012; Wopken 2014b), three models for radiation‐induced hypothyroidism (Boomsma 2012; Cella 2012; Ronjom 2013) and three models for temporal lobe injury (OuYang 2023; Wen 2021; Yang 2023a). Those models generally showed good discriminative performance. However, as seen in previous reviews of prediction models, information on their calibration performance was less often reported, meaning that it remains unclear to what extent these models are truly externally valid. ROB assessment showed that the models published by Hansen (Hansen 2020) for oral mucositis, Wen (Wen 2021) for temporal lobe injury, Sheikh (Sheikh 2019) for xerostomia, Wopken (Wopken 2014a) for tube‐feeding dependence, Hamada (Hamada 2023) for acute dermatitis, Van den Bosch (Van den Bosch 2021) for dysphagia, aspiration, xerostomia, sticky saliva, loss of taste, mucositis, hoarseness, speech problems, oral pain, throat pain, jaw pain, weight loss, nausea and vomiting, and fatigue, and Van Rijn‐Dekker for xerostomia (Van Rijn‐Dekker 2023) had the highest quality amongst all 617 developed models. Based on the currently available evidence, we consider those ten already validated prediction models to have the highest potential for use in clinical practice, followed by the models presented in the seven articles considered of the highest quality.
Implications for research
This review reveals that the development of new prediction models is also an ongoing practice for toxicity prediction after radiotherapy for patients with head and neck cancer. This is commonly observed when reviewing prediction models, regardless of medical field of interest (Damen 2016; Kleinrouweler 2016; Van Beek 2021; Wynants 2020), and clearly shows that a shift is needed towards external validation and adaptation of existing prediction models. Only then will the true value of the many developed models for daily practice be clarified. Ideally, these validation studies are performed by using high‐quality, prospectively collected data in large samples to allow for reliable model validation (Riley 2022). Consecutively, impact studies are warranted to quantify the effect of an NTCP model on physicians' behaviour and patient outcome (Moons 2009).
In general, ROB was high in most development studies, and was mainly due to inappropriate analysis methods or omission of important statistical considerations. This indicates that improvements are needed in conducting, as well as reporting, of development (and validation) studies that focus on NTCP prediction. Improvements can be achieved by consideration of PROBAST (Moons 2019; Wolff 2019), and better reporting by adherence to the TRIPOD statement (Collins 2015b; Moons 2015).
What's new
| Date | Event | Description |
|---|---|---|
| 11 September 2026 | Amended | We added eligible studies, models, and external validations that had been missed in the previous version, and removed several duplicate model/validation records. Accordingly, we updated the analysis datasets, descriptive results in the text, figures, tables, SoF, and appendices, and checked consistency with the included/excluded studies and externally validated models. |
History
Protocol first published: Issue 5, 2021 Review first published: Issue 9, 2025
Acknowledgements
This review was performed as part of the HTx project. HTx is a Horizon 2020 project supported by the European Union, lasting for five years from January 2019. The main aim of HTx is to create a framework for the Next Generation Health Technology Assessment (HTA) to support patient‐centred, societally oriented, real‐time decision‐making on access to and reimbursement for health technologies throughout Europe.
We would like to thank Dr. Lifang Liu (European Organisation for Research and Treatment of Cancer (EORTC) Headquarters) and Dr. Roel JHM Steenbakkers (Department of Radiation Oncology, University Medical Center, Groningen, University of Groningen) for their feedback from a clinical perspective.
Editorial and peer‐reviewer contributions The following people conducted the editorial process for this article: • Sign‐off Editor (final editorial decision): Dr. Farid Foroutan, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada; Toby Lasserson, Cochrane; • Managing Editor (selected peer reviewers, provided editorial guidance to authors, edited the article): Anne‐Marie Stephani, Cochrane Central Editorial Service; • Editorial Assistant (conducted editorial policy checks, collated peer‐reviewer comments and supported editorial team): Lisa Wydrzynski, Cochrane Central Editorial Service; • Copy Editor (copy editing and production): Anne Lethaby, Cochrane Central Production Service; • Peer‐reviewers (provided comments and recommended an editorial decision): Wilber Edison Bernaola‐ Paredes, Department of Radiation Oncology, A.C. Camargo Cancer Center, Sao Paulo, Brazil (clinical review); Jianjun Ren, West China Hospital, Sichuan University (clinical review); Prof. Dr. Nicole Skoetz, University Hospital of Cologne (consumer review); Maria‐Inti Metzendorf, Institute of General Practice, Medical Faculty of the Heinrich Heine University, Düsseldorf, Germany (search review).
Appendices
Appendix 1. Search strategy
Ovid MEDLINE
| # | Searches |
| 1 | exp "Head and Neck Neoplasms"/ |
| 2 | (((upper adj aerodigestive adj tract) or uadt) and (cancer* or carcinom* or tumor* or tumour* or neoplas* or malignan* or metasta*)).ti,ab,kf. |
| 3 | (ent adj4 (cancer* or carcinom* or tumor* or tumour* or neoplas* or malignan* or metasta*)).ti,ab,kf. |
| 4 | ((head or neck or oral or tongue or lip or tonsil or nasal or oropharyn* or pharyn* or laryn* or throat or EAR or glotti* or nasopharyn* or hypopharyn* or maxillofacial or paranasal‐sinus) adj4 (cancer* or carcinom* or tumor* or tumour* or neoplas* or malignan* or metasta*)).ti,ab,kf. |
| 5 | (hnscc or scchn).ti,ab,kf. |
| 6 | 1 or 2 or 3 or 4 or 5 |
| 7 | exp Radiotherapy/ or RADIATION ONCOLOGY/ or exp Combined Modality Therapy/ or (radiotherap$ or chemoradiotherap$ or radiation‐therap$ or brachytherap$ or brachy‐therap$ or irradiat$ or proton or photon or radio‐therap$ or chemo‐radio‐therap$ or irradiat$ or radiat$ or (external* adj3 beam*) or ((multimodal$ or multi‐modal$) adj3 (treat$ or therap$)) or (combi$ adj3 modalit$)).ti,ab,kf. |
| 8 | 6 and 7 |
| 9 | (head adj2 neck adj3 (radiation or radiotherap*)).ti,ab,kf. |
| 10 | 8 or 9 |
| 11(Geersing 2012) | Validat$.tw. or Predict$.ti. or Rule$.tw. or ((Predict$ or probabilit*) and (Outcome$ or Risk$ or Model$)).tw. or ((History or Variable$ or Criteria or Scor$ or Characteristic$ or Finding$ or Factor$) and (Predict$ or Model$ or Decision$ or Identif$ or Prognos$)).tw. or (Decision$.tw. and ((Model$ or Clinical$).tw. or exp Models, Statistical/)) or (Prognostic and (History or Variable$ or Criteria or Scor$ or Characteristic$ or Finding$ or Factor$ or Model$)).tw. or ("Stratification" or "Discrimination" or "Discriminate" or c‐statistic or "Area under the curve" or "AUC" or "Calibration" or "Indices" or "Algorithm" or "Multivariable" or NTCP).tw. |
| 12 | (safe or safety or side‐effect* or undesirable effect* or treatment emergent or tolerability or toxicity or adrs or (adverse adj2 (effect or effects or reaction or reactions or event or events or outcome or outcomes))).ti,ab. |
| 13 | (xerostomia or dry‐mouth or (salivary adj2 (dysfunction or flow)) or "sticky saliva" or (salivary‐duct adj2 inflammation) or dysphagia or (swallowing adj2 (dysfunction or problems)) or tube‐feeding or esophagitis or "oral mucositis" or (mucosal adj2 (reactions or ulcers)) or atrophia or hypothyroidism or (thyroid adj3 dysfunction) or hearing‐loss or deafness or tinnitus or ((laryngeal or mandibular) adj2 (dysfunction or necrosis)) or "voice problems" or hoarseness or chondronecrosis or osteoradionecrosis).ti,ab. or (exp Neoplasms, Radiation‐Induced/ or exp Vision Disorders/ or exp Radiation Injuries/ or exp Carotid Stenosis/) or ((radiation‐induced adj (cancer or neoplasm* or tumo?r)) or (visual adj impair*) or retinopath* or (temporal‐lobe adj injury) or carotid stenosis).ti,ab,kf. |
| 14 | (ae or co).fs. |
| 15 | 12 or 13 or 14 |
| 16 | ("expected ratio" or "observed ratio" or "E:O ratio" or "Hosmer‐Lemeshow" or "H‐L test" or (ROC or AUC or AUROC) or (Area adj3 (Receiver or ROC or curve)) or classif* or ((negative or positive) adj2 predictive‐value*) or clinical‐accuracy or (sensitivity or specificity or PPV or NPV) or (performance adj3 (classification or classifier or clinical or accuracy or validation or metrics or diagnostic)) or (calibrat* and (plot* or curve* or slope* or model or models)) or decision‐curve or (discriminability or c‐index or c‐statistic or concordance or DCA) or ((discrimination or discriminative or discriminatory) adj3 (accuracy or ability or performance or value or model or models or power or capacity or capabilit* or efficiency))).ti,ab,kf. |
| 17 | dose‐response.ti,ab,kf. |
| 18 | exp ROC Curve/ or exp Area Under Curve/ or exp "artificial intelligence"/ or "neural networks (computer)"/ or exp data mining/ |
| 19 | (((machine or statistical) adj (learning or model*)) or multilayer‐perceptron* or random‐forest* or (bayes* adj2 network*) or (support‐vector adj machine*) or nearest‐neighbor* or elastic‐net or naive‐bayes* or ((classification or regression or estimation or decision) adj3 tree) or ridge or kernel or ensemble or bagging or bagged or boosting or boosted or fuzzy).ti,ab,kf. |
| 20 | 11 or 16 or 17 or 18 or 19 |
| 21 | 10 and 15 and 20 |
| 22 | (exp animals/ not humans/) or (case reports or review).pt. |
| 23 | 21 not 22 |
Embase.com
| No. | Query |
| #12 | #11 AND [embase]/lim |
| #11 | #9 NOT #10 |
| #10 | (rat:ti OR rats:ti OR mouse:ti OR mice:ti OR swine:ti OR porcine:ti OR murine:ti OR sheep:ti OR lambs:ti OR pigs:ti OR piglets:ti OR rabbit:ti OR rabbits:ti OR cat:ti OR cats:ti OR dog:ti OR dogs:ti OR cattle:ti OR bovine:ti OR monkey:ti OR monkeys:ti OR trout:ti OR marmoset*:ti) AND 'animal experiment'/exp OR ('animal experiment'/exp NOT ('human experiment'/exp OR 'human'/exp)) OR 'case report'/exp OR 'review'/it |
| #9 | #1 AND #2 AND #8 |
| #8 | #3 OR #4 OR #5 OR #6 OR #7 |
| #7 | (((machine OR statistical) NEAR/1 (learning OR model*)):ti,ab) OR 'multilayer perceptron*':ti,ab OR 'random forest*':ti,ab OR ((bayes* NEAR/2 network*):ti,ab) OR (('support vector' NEAR/1 machine*):ti,ab) OR 'nearest neighbor*':ti,ab OR 'elastic net':ti,ab OR 'naive bayes*':ti,ab OR (((classification OR regression OR estimation OR decision) NEAR/3 tree):ti,ab) OR ridge:ti,ab OR kernel:ti,ab OR ensemble:ti,ab OR bagging:ti,ab OR bagged:ti,ab OR boosting:ti,ab OR boosted:ti,ab OR fuzzy:ti,ab |
| #6 | 'dose response':ti,ab |
| #5 | 'area under the curve'/exp OR 'receiver operating characteristic'/exp OR 'machine learning'/exp OR 'machine learning' OR 'artificial intelligence'/exp |
| #4 | 'expected ratio':ti,ab OR 'observed ratio':ti,ab OR 'e:o ratio':ti,ab OR 'hosmer‐lemeshow':ti,ab OR 'h‐l test':ti,ab OR roc:ti,ab OR auc:ti,ab OR auroc:ti,ab OR ((area NEAR/3 (receiver OR roc OR curve)):ti,ab) OR classif*:ti,ab OR (((negative OR positive) NEAR/2 'predictive value*'):ti,ab) OR 'clinical accuracy':ti,ab OR sensitivity:ti,ab OR specificity:ti,ab OR ppv:ti,ab OR npv:ti,ab OR ((performance NEAR/3 (classification OR classifier OR clinical OR accuracy OR validation OR metrics OR diagnostic)):ti,ab) OR (calibrat*:ti,ab AND (plot*:ti,ab OR curve*:ti,ab OR slope*:ti,ab OR model:ti,ab OR models:ti,ab)) OR 'decision curve':ti,ab OR discriminability:ti,ab OR 'c index':ti,ab OR 'c statistic':ti,ab OR concordance:ti,ab OR dca:ti,ab OR (((discrimination OR discriminative OR discriminatory) NEAR/3 (accuracy OR ability OR performance OR value OR model OR models OR power OR capacity OR capabilit* OR efficiency)):ti,ab) |
| #3 | validat*:ti,ab,kw OR predict*:ti OR rule*:ti,ab,kw OR ((predict*:ti,ab,kw OR probabilit*:ti,ab,kw) AND (outcome*:ti,ab,kw OR risk*:ti,ab,kw OR model*:ti,ab,kw)) OR ((history:ti,ab,kw OR variable*:ti,ab,kw OR criteria:ti,ab,kw OR scor*:ti,ab,kw OR characteristic*:ti,ab,kw OR finding*:ti,ab,kw OR factor*:ti,ab,kw) AND (predict*:ti,ab,kw OR model*:ti,ab,kw OR decision*:ti,ab,kw OR identif*:ti,ab,kw OR prognos*:ti,ab,kw)) OR (decision*:ti,ab,kw AND (model*:ti,ab,kw OR clinical*:ti,ab,kw OR 'statistical model'/exp OR 'statistical model')) OR (prognostic:ti,ab,kw AND (history:ti,ab,kw OR variable*:ti,ab,kw OR criteria:ti,ab,kw OR scor*:ti,ab,kw OR characteristic*:ti,ab,kw OR finding*:ti,ab,kw OR factor*:ti,ab,kw OR model*:ti,ab,kw)) OR 'stratification':ti,ab,kw OR 'discrimination':ti,ab,kw OR 'discriminate':ti,ab,kw OR 'c statistic':ti,ab,kw OR 'area under the curve':ti,ab,kw OR 'auc':ti,ab,kw OR 'calibration':ti,ab,kw OR 'indices':ti,ab,kw OR 'algorithm':ti,ab,kw OR 'multivariable':ti,ab,kw OR ntcp:ti,ab,kw |
| #2 | safe:ti,ab OR safety:ti,ab OR 'side effect*':ti,ab OR 'undesirable effect*':ti,ab OR 'treatment emergent':ti,ab OR tolerability:ti,ab OR toxicity:ti,ab OR adrs:ti,ab OR ((adverse NEAR/2 (effect OR effects OR reaction OR reactions OR event OR events OR outcome OR outcomes)):ti,ab) OR xerostomia:ti,ab OR 'dry mouth':ti,ab OR ((salivary NEAR/2 (dysfunction OR flow)):ti,ab) OR 'sticky saliva':ti,ab OR (('salivary duct' NEAR/2 inflammation):ti,ab) OR dysphagia:ti,ab OR ((swallowing NEAR/2 (dysfunction OR problems)):ti,ab) OR 'tube feeding':ti,ab OR esophagitis:ti,ab OR 'oral mucositis':ti,ab OR ((mucosal NEAR/2 (reactions OR ulcers)):ti,ab) OR atrophia:ti,ab OR hypothyroidism:ti,ab OR ((thyroid NEAR/3 dysfunction):ti,ab) OR 'hearing loss':ti,ab OR deafness:ti,ab OR tinnitus:ti,ab OR (((laryngeal OR mandibular) NEAR/2 (dysfunction OR necrosis)):ti,ab) OR 'voice problems':ti,ab OR hoarseness:ti,ab OR chondronecrosis:ti,ab OR osteoradionecrosis:ti,ab OR 'adverse drug reaction':lnk OR 'side effect':lnk OR 'adverse device effect':lnk OR 'radiation induced neoplasm'/exp OR 'radiation injury'/exp OR 'retinopathy'/exp OR 'visual impairment'/exp OR 'carotid artery obstruction'/exp OR (((('radiation induced' NEAR/1 (cancer OR neoplasm* OR tumor* OR tumours)):ti,ab,kw) OR ((visual NEAR/1 impair*):ti,ab,kw) OR retinopath*:ti,ab,kw OR (('temporal lobe' NEAR/1 injury):ti,ab,kw) OR carotid:ti,ab,kw) AND stenosis:ti,ab,kw) |
| #1 | ('head and neck cancer'/exp OR 'head and neck cancer' OR ((((upper NEAR/1 'aerodigestive tract'):ti,ab) OR uadt:ti,ab) AND (cancer*:ti,ab OR carcinom*:ti,ab OR tumor*:ti,ab OR tumour*:ti,ab OR neoplas*:ti,ab OR malignan*:ti,ab OR metasta*:ti,ab)) OR ((ent NEAR/4 (cancer* OR carcinom* OR tumor* OR tumour* OR neoplas* OR malignan* OR metasta*)):ti,ab) OR (((head OR neck OR oral OR tongue OR lip OR tonsil OR nasal OR oropharyn* OR pharyn* OR laryn* OR throat OR ear OR glotti* OR nasopharyn* OR hypopharyn* OR maxillofacial OR 'paranasal sinus') NEAR/4 (cancer* OR carcinom* OR tumor* OR tumour* OR neoplas* OR malignan* OR metasta*)):ti,ab) OR hnscc:ti,ab OR scchn:ti,ab) AND ('radiotherapy'/exp OR 'radiotherapy' OR 'radiation oncology'/exp OR 'radiation oncology' OR 'multimodality cancer therapy'/exp OR 'multimodality cancer therapy' OR radiotherap*:ti,ab OR radio‐therap*:ti,ab OR chemoradiotherap*:ti,ab OR chemo‐radio‐therap*:ti,ab OR 'radiation therap*':ti,ab OR brachytherap*:ti,ab OR brachy‐therap*:ti,ab OR irradiat*:ti,ab OR proton:ti,ab OR photon:ti,ab OR 'radio therap*':ti,ab OR radiat*:ti,ab OR ((extrenal* NEAR/3 beam*):ti,ab) OR (((multimodal* OR 'multi modal*') NEAR/3 (treat* OR therap*)):ti,ab) OR ((combi* NEAR/3 modal*):ti,ab)) |
WHO ICTRP
(head OR neck OR oral OR tongue OR lip OR tonsil OR nasal OR oropharyn* OR pharyn* OR laryn* OR throat OR ear OR glotti* OR nasopharyn* OR hypopharyn* OR maxillofacial OR 'paranasal sinus') AND (cancer* OR carcinom* OR tumor* OR tumour* OR neoplas* OR malignan* OR metasta*) AND model*
Appendix 2. Preliminary study selection, data extraction and risk of bias forms for development and internal validation studies
| Domain | Item to answer | Explanation/classification | Reference | |
| General information | Data extracted by | Your name | ||
| First author | Surname of the 1st author | |||
| Year of publication | yyyy | |||
| Sub ID | If the study reports multiple models (e.g. different outcomes, different presentations, etc.), enter the items in different rows. For example, if there are two models for 30‐day and 6‐month mortality, label them as "1st author's surname‐1" and "1st author's surname‐2". Also, if the model was externally validated, enter the information about the external validation in another row. | |||
| Sub ID details | Describe the details of the models to distinguish from each other (for example of the left cell, describe as "model for predicting 30‐day mortality" and "external validation", respectively). | |||
| Study characteristics | Study description | Purpose | ‐ Development only ‐ Development and internal validation ‐ Development and external validation ‐ Others (specify) | CHARMS domain 1: source of data |
| Number of centres | 1, 2, 3... | |||
| Location | ‐ North America ‐ Europe ‐ Asia ‐ Australia ‐ Africa ‐ South America ‐ Central America ‐ Combination | |||
| Participants | Study description | Data source | ‐ Prospective cohort ‐ Retrospective cohort ‐ Nested case‐control ‐ Non‐nested case‐control ‐ Case‐cohort ‐ RCT ‐ Registry data ‐ Others (specify) ‐ Unclear | CHARMS domain 2: participants |
| Recruitment method | Were patients recruited consecutively? | |||
| Start date of recruitment period | dd‐mm‐yyyy If the exact date is not reported (e.g. only year is reported), just enter available information (e.g. yyyy). | |||
| End date of recruitment period | dd‐mm‐yyyy If the exact date is not reported (e.g. only year is reported), just enter available information (e.g. yyyy). | |||
| Inclusion criteria | Copy and paste from the article text | |||
| Exclusion criteria | Copy and paste from the article text | |||
| Primary site of cancer | Primary site ‐ Oral cavity ‐ Larynx ‐ Hypopharynx ‐ Oropharynx ‐ Nasopharynx ‐ Salivary glands ‐ Nasal cavity ‐ Paranasal sinus ‐ Eye ‐ Lymphoma ‐ Soft tissue ‐ Skull base ‐ Ear ‐ Lip ‐ Thyroid ‐ Others (specify) | |||
| Type of cancer | Pathology of cancer ‐ SCC ‐ Adenocarcinoma ‐ Melanoma ‐ Sarcoma ‐ Lymphoma ‐ Others (specify) ‐ Unclear | |||
| TNM classification | Which version of the classification was used? ‐ 7th version (before 2017) ‐ 8th version (after 2017) | |||
| Stage | Based on TNM classification (I, II, III, IV…) | |||
| Type of radiotherapy | ‐ IMRT ‐ VMAT ‐ 3D conformal ‐ Proton ‐ Electron ‐ Combined ‐ Others (specify) ‐ Unclear | |||
| Surgery | ‐ No surgery ‐ Pre‐surgery radiotherapy ‐ Post‐surgery radiotherapy ‐ Non‐specified ‐ Others (specify) | |||
| Type of surgery if specified | Detailed information about surgery (e.g. laryngectomy) Copy and paste from the paper | |||
| Chemotherapy | At baseline: ‐ No chemotherapy ‐ Concurrent chemotherapy ‐ Chemotherapy before radiotherapy ‐ Chemotherapy after radiotherapy ‐ Case mix with different radiotherapy and chemotherapy combinations ‐ Non‐specified ‐ Others (specify) | |||
| Molecular‐targeted therapy | At baseline: ‐ No molecular‐targeted therapy ‐ Concurrent molecular‐targeted therapy ‐ Molecular‐targeted therapy before radiotherapy ‐ Molecular‐targeted therapy after radiotherapy ‐ Case mix with different radiotherapy and molecular therapy combinations ‐ Non‐specified ‐ Others (specify) | |||
| Other specific patient characteristics | ||||
| Signalling questions | Were appropriate data sources used, e.g. cohort, RCT, or nested case‐control study data? | ‐ Yes/probably yes: If a cohort design (including RCT or proper registry data) or a nested case‐control or case‐cohort design (with proper adjustment of the baseline risk/hazard in the analysis) has been used ‐ No/probably no: If a non‐nested case‐control design has been used ‐ No information: If the method of participant sampling is unclear | PROBAST step 3, question 1.1 | |
| Were all inclusions and exclusions of participants appropriate? | ‐ Yes/probably yes: If inclusion and exclusion of participants is appropriate, so participants correspond to unselected participants of interest ‐ No/probably no: If participants are included who would already have been identified as having the outcome and so are no longer participants at risk of developing outcome, or if specific subgroups are excluded that may have altered the performance of the prediction model for the intended target population ‐ No information: When there is no information on whether inappropriate inclusions or exclusions took place | PROBAST step 3, question 1.2 | ||
| Risk of bias | Risk of bias introduced by participants or data sources | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be "Low risk of bias", but specific reasons should be provided why the risk of bias can be considered low ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias, except if defined as being at low risk of bias above ‐ Unclear risk of bias: If relevant information is missing for some of the signalling questions and none of the signalling questions is judged to put this domain at high risk of bias | PROBAST step 3, risk of bias domain 1 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 1 | ||
| Applicability | Concerns that the included participants or the setting do not match the review question | ‐ Low concerns about applicability: Included participants and clinical setting match the review question ‐ High concerns about applicability: Included participants and clinical setting are different from the review question ‐ Unclear concerns about applicability: If relevant information about the participants and clinical setting is not reported | PROBAST step 3, applicability domain 1 | |
| Rationale of applicability rating | Provide specific reasons for classification of applicability if necessary | PROBAST step 3, applicability domain 1 | ||
| Predictors | Study descriptions | List candidate predictors | A; B; C; … (not the predictors finally included in the model, but the candidate predictors which were possibly included in the model. Separate each by ;) | CHARMS domain 4: Candidate predictors |
| List predictors included in the final model | A; B; C; … (separate each by ;) | |||
| Number of predictors in the final model | 1, 2, 3… | |||
| Additional degrees of freedom of predictors | If a categorical variable has more than 2 categories, count all extra categories above 2. So, for 3 categories, count 1 (3‐2), for 5 categories, count 3 (5‐2). Do this for all categorical variables and add numbers. Add the number of interaction terms, and the number of degrees of freedom spent modelling non‐linearities | |||
| Signalling questions | Were predictors defined and assessed in a similar way for all participants? | ‐ Yes/probably yes: If definitions of predictors and their assessment are similar for all participants ‐ No/probably no: If different definitions are used for the same predictor or if predictors requiring subjective interpretation are assessed by differently experienced assessors ‐ No information: If there is no information on how predictors were defined or assessed | PROBAST step 3, question 2.1 | |
| Were predictor assessments made without knowledge of outcome data? | ‐ Yes/probably yes: If outcome information is stated as not used during predictor assessment or is clearly not (yet) available to those assessing predictors ‐ No/probably no: If it is clear that outcome information is used when assessing predictors ‐ No information: No information on whether predictors are assessed without knowledge of outcome information | PROBAST step 3, question 2.2 | ||
| Are all predictors available at the time the model is intended to be used? | ‐ Yes/probably yes: All included predictors would be available at the time the model is intended to be used for prediction ‐ No/probably no: Predictors would not be available at the time that the model is intended to be used for prediction ‐ No information: No information on whether predictors would be available at the time the model is intended to be used for prediction | PROBAST step 3, question 2.3 | ||
| Risk of bias | Risk of bias introduced by predictors or their assessment | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be "Low risk of bias" but specific reasons should be provided why the risk of bias can be considered low, e.g. use of objective predictors not requiring subjective interpretation ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias ‐ Unclear risk of bias: If relevant information is missing for some of the signalling questions and none of the signalling questions is judged to put the domain at high risk of bias | PROBAST step 3, risk of bias domain 2 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 2 | ||
| Applicability | Concern that the definition, assessment, or timing of predictors in the model do not match the review question. | ‐ Low concerns about applicability: Definition, assessment, and timing of predictors match the review question ‐ High concerns about applicability: Definition, assessment, or timing of predictors are different from the review question ‐ Unclear concerns about applicability: If relevant information about the predictors is not reported | PROBAST step 3, applicability domain 2 | |
| Rationale of applicability rating | Provide specific reasons for classification of applicability if necessary | PROBAST step 3, applicability domain 1 | ||
| Outcome | Outcome | ‐ Oral mucositis ‐ Tube‐feeding dependence ‐ Xerostomia ‐ Disgeusia ‐ Dysphagia ‐ Oesophagitis ‐ Hypothyroidism ‐ Hearing loss ‐ Tinnitus ‐ Visual loss ‐ Wound healing complications ‐ Aspiration ‐ Dermatitis ‐ Head and neck pain ‐ Hoarseness ‐ Oral pain ‐ Salivary duct inflammation ‐ Sore throat ‐ Weight loss ‐ Fatigue ‐ Osteoradionecrosis ‐ Neurocognitive dysfunction ‐ Cerebrovascular events ‐ Speech problems ‐ Jaw pain ‐ Nausea and vomiting ‐ Others (specify) | CHARMS domain 3: Outcome | |
| Definition | Describe the definition of the outcome (copy and paste from the original article) | |||
| Time point of the outcome | Days/months/years | |||
| Signalling questions | Was the outcome determined appropriately? | ‐ Yes/probably yes: If a method of outcome determination has been used which is considered optimal or acceptable by guidelines or previous publications on the topic. Note: this is about the level of measurement error within the method of determining the outcome (see concerns for applicability about whether the definition of the outcome method is appropriate) ‐ No/probably no: If a clearly suboptimal method has been used that causes unacceptable errors in determining outcome status in participants ‐ No information: No information on how the outcome is determined | PROBAST step 3, question 3.1 | |
| Was a prespecified or standard outcome definition used? | ‐ Yes/probably yes: If the method of outcome determination is objective, or if a standard outcome definition is used, or if prespecified categories are used to group outcomes ‐ No/probably no: If the outcome definition is not standard and not prespecified ‐ No information: No information on whether the outcome definition is prespecified or standard | PROBAST step 3, question 3.2 | ||
| Were predictors excluded from the outcome definition? | ‐ Yes/probably Yes: If none of the predictors are included in the outcome definition ‐ No/probably No: If ≥ 1 of the predictors forms part of the outcome definition ‐ No information: No information on whether predictors are excluded from the outcome definition | PROBAST step 3, question 3.3 | ||
| Was the outcome defined and determined in a similar way for all participants? | ‐ Yes/probably yes: If outcomes are defined and determined in a similar way for all participants ‐ No/probably no: If outcomes are clearly defined and determined in a different way for some participants ‐ No information: No information on whether predictors are excluded from the outcome definition | PROBAST step 3, question 3.4 | ||
| Was the outcome determined without knowledge of predictor information? | ‐ Yes/probably yes: If predictor information is not known when determining the outcome status, or outcome status determination is clearly reported as determined without knowledge of predictor information ‐ No/probably no: If it is clear that predictor information is used when determining the outcome status ‐ No information: No information on whether the outcome is determined without knowledge of predictor information | PROBAST step 3, question 3.5 | ||
| Was the time interval between predictor assessment and outcome determination appropriate? | ‐ Yes/probably yes: If the time interval between predictor assessment and outcome determination is appropriate to enable the correct type and representative number of relevant outcomes to be recorded, or if no information on the time interval is required to allow a representative number of the relevant outcomes to occur or if predictor assessment and outcome determination are from information taken within an appropriate time interval ‐ No/probably no: If the time interval between predictor assessment and outcome determination is too short or too long to enable the correct type and representative number of relevant outcomes to be recorded ‐ No information: If no information is provided on the time interval between predictor assessment and outcome determination | PROBAST step 3, question 3.6 | ||
| Risk of bias | Risk of bias introduced by the outcome or its determination | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be low risk of bias, but specific reasons should be provided why the risk of bias could be considered low, e.g. when the outcome is determined with knowledge of predictor information, but the outcome assessment does not require much interpretation by the assessor (e.g. death regardless of cause) ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias ‐ Unclear risk of bias: If relevant information about the outcome is missing for some of the signalling questions and none of the signalling questions is judged to put this domain at high risk of bias | PROBAST step 3, risk of bias domain 3 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 3 | ||
| Applicability | Concerns that the outcome definition, timing, or determination do not match the review question | ‐ Low concerns about applicability: Outcome definition, timing, and method of determination define the outcome as intended by the review question ‐ High concerns about applicability: Choice of outcome definition, timing, and method of outcome determination define another outcome as intended by the review question ‐ Unclear concerns about applicability: If relevant information about the outcome, timing, and method of determination is not reported | PROBAST step 3, applicability domain 3 | |
| Rationale of applicability rating | Provide specific reasons for classification of applicability if necessary | PROBAST step 3, applicability domain 1 | ||
| Analyses | Number of participants | Specify the number of participants included in the analysis | CHARMS domain 5: Sample size | |
| Number of participants with the outcome | Specify the number of participants with the outcome | |||
| Event per variable | Filled automatically | |||
| Handling of missing data | How were missing data handled in the analysis? Complete‐case analysis ‐ Single imputation ‐ Multiple imputation ‐ Sensitivity analysis ‐ Variable omission ‐ Others (specify) ‐ Not reported | CHARMS domain 6: Missing data | ||
| Number of participants with any missing predictor/outcome values | Specify number. When missing data are exclusion criteria, add 0. If not reported, write "NR" | |||
| Modelling method | ‐ Linear regression ‐ Logistic regression ‐ Multinomial/polytomous regression ‐ Ordinal regression ‐ Cox proportional hazards regression ‐ Accelerated failure time analysis (e.g. Weibull model) ‐ Other parametric survival models ‐ Others (specify) ‐ Unclear | |||
| Method to deal with continuous variables | ‐ Linear ‐ Non‐linear transformations ‐ Categorised ‐ Others (specify) | CHARMS domain 7: Model development | ||
| Method for selection of predictors for inclusion in multivariable analysis | How were the predictors statistically selected before the modelling process? ‐ All candidate predictors are used ‐ Pre‐selection based on univariable analysis ‐ Principal component analysis ‐ Variance inflation factor ‐ Multicollinearity ‐ Others (specify) ‐ Not reported ‐ Unclear | |||
| Method for selection of predictors during modelling process | How were the predictors statistically selected during the modelling process? ‐ All predictors forced in the model ‐ BW stepwise selection ‐ FW stepwise selection ‐ Added value ‐ Lasso/ridge ‐ Not applicable ‐ Others (specify) ‐ Not reported ‐ Unclear | |||
| Criteria for selection of predictors | E.g. P < 0.05, AIC < 2, etc. Write "NA" if not applicable, "NR" if it is not reported | |||
| Technique used for internal validation | What kind of technique was used for internal validation? ‐ Random split of dataset into development and testing datasets ‐ Non‐random split of dataset into development and testing sets ‐ Internal validation by resampling of the same dataset (e.g. bootstrap (specify the number of samples) ‐ Cross‐validation ‐ Unclear method of splitting data ‐ Not applicable ‐ Other (specify) | |||
| Shrinkage | Was any shrinkage method used? ‐ Not performed ‐ Heuristic shrinkage (uniform) ‐ Calibration slope assessed with bootstrapping (uniform) ‐ Penalised maximum likelihood estimation ‐ Lasso/ridge ‐ Others (specify) ‐ Unclear | |||
| Model presentation | How was the model presented? ‐ Mathematical equations (only coefficients) ‐ Mathematical equations (both intercept and coefficients) ‐ Simple scoring system ‐ Nanogram ‐ Web calculator ‐ Others (specify) | |||
| Signalling questions | Were there a reasonable number of participants with the outcome? | ‐ Yes/probably yes: For model development studies, if the number of participants with the outcome relative to the number of candidate predictors parameters is ≥ 20 (EPV ≥ 20). For EPV between 10 and 20, the item should be rated as either probably yes or probably no, depending on the outcome frequency, overall model performance, and distribution of the predictors in the model ‐ No/probably no: For model development studies, if the number of participants with the outcome relative to the number of candidate predictor parameters is < 10 (EPV < 10) ‐ No information: For model development studies, no information on the number of candidate predictor parameters or number of participants with the outcome, such that the EPV cannot be calculated | PROBAST step 3, question 4.1 | |
| Were continuous and categorical predictors handled appropriately? | ‐ Yes/probably yes: If continuous predictors are not converted into ≥ 2 categories when included in the model (i.e. dichotomised or categorised), or if continuous predictors are examined for non‐linearity using, for example, fractional polynomials or restricted cubic splines, or if categorical predictor groups are defined using a prespecified method. For model validation studies, if continuous predictors are included using the same definitions or transformations, and categorical variables are categorised using the same cut points, as compared with the development study ‐ No/probably no: If categorical predictor group definitions do not use a prespecified method. For model development studies, if continuous predictors are converted into ≥ 2 categories when included in the model. For model validation studies, if continuous predictors are included using different definitions or transformations, or categorical variables are categorised using different cut points, as compared with the development study ‐ No information: No information on whether continuous predictors are examined for non‐linearity and no information on how categorical predictor groups are defined. For model validation studies, no information on whether the same definitions or transformations and the same cut points are used, as compared with the development study | PROBAST step 3, question 4.2 | ||
| Were all enroled participants included in the analysis? | ‐ Yes/probably yes: If all participants enroled in the study are included in the data analysis ‐ No/probably no: If some or a subgroup of participants are inappropriately excluded from the analysis ‐ No information: No information on whether all enroled participants are included in the analysis | PROBAST step 3, question 4.3 | ||
| Were participants with missing data handled appropriately? | ‐ Yes/probably yes: If there are no missing values of predictors or outcomes and the study explicitly reports that participants are not excluded on the basis of missing data, or if missing values are handled using multiple imputations ‐ No/probably no: If participants with missing data are omitted from the analysis, or if the method of handling missing data is clearly flawed, e.g. missing indicator method or inappropriate use of last value carried forward, or if the study has no explicit mention of methods to handle missing data ‐ No information: If there is insufficient information to determine if the method of handling missing data is appropriate | PROBAST step 3, question 4.4 | ||
| Was selection of predictors based on univariable analysis avoided? | ‐ Yes/probably yes: If the predictors are not selected on the basis of univariable analysis prior to multivariable modelling ‐ No/probably no: If the predictors are selected on the basis of univariable analysis prior to multivariable modelling ‐ No information: If there is no information to indicate that univariable selection is avoided | PROBAST step 3, question 4.5 | ||
| Were complexities in the data (e.g. censoring, competing risks, sampling of control participants) accounted for appropriately? | ‐ Yes/probably yes: If any complexities in the data are accounted for appropriately, or if it is clear that any potential data complexities have been identified appropriately as unimportant ‐ No/probably no: If complexities in the data that could affect model performance are ignored ‐ No information: No information is provided on whether complexities in the data are present or accounted for appropriately if present | PROBAST step 3, question 4.6 | ||
| Were relevant model performance measures evaluated appropriately? | ‐ Yes/probably yes: If both calibration and discrimination are evaluated appropriately (including relevant measures tailored for models predicting survival outcomes) ‐ No/probably no: If both calibration and discrimination are not evaluated, or if only goodness‐of‐fit tests, such as the Hosmer‐Lemeshow test, are used to evaluate calibration, or if models predicting survival outcomes performance measures accounting for censoring ae not used, or if classification measures (like sensitivity, specificity, or predictive values) are presented using predicted probability thresholds derived from the dataset at hand ‐ No information: Either calibration or discrimination are not reported, or no information is provided on whether appropriate performance measures for survival outcomes are used (e.g. references to relevant literature or specific mention of methods, such as using Kaplan‐Meier estimates), or no information on thresholds for estimating classification measures is given | PROBAST step 3, question 4.7 | ||
| Were model overfitting and optimism in model performance accounted for? | ‐ Yes/probably yes: If internal validation techniques, such as bootstrapping and cross‐validation, including all model development procedures, have been used to account for any optimism in model fitting, and subsequent adjustment of the model performance estimates has been applied ‐ No/probably no: If no internal validation has been performed, or if internal validation consists only of a single random split‐sample of participant's data, or if the bootstrapping or cross‐validation does not include all model development procedures including any variable selection ‐ No information: No information is provided on whether internal validation techniques, including all model development procedures, have been applied | PROBAST step 3, question 4.8 | ||
| Do predictors and their assigned weights in the final model correspond to the results from the reported multivariable analysis? | ‐ Yes/probably yes: If the predictors and regression coefficients in the final model correspond to reported results from multivariable analysis ‐ No/probably no: If the predictors and regression coefficients in the final model do not correspond to reported results from multivariable analysis ‐ No information: If it is unclear whether the regression coefficients in the final model correspond to reported results from multivariable analysis | PROBAST step 3, question 4.9 | ||
| Risk of bias | Risk of bias introduced by the analysis | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be low risk of bias, but specific reasons should be provided why the risk of bias can be considered low ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias ‐ Unclear risk of bias: If relevant information about the analysis is missing for some of the signalling questions, but none of the signalling questions is judged to put the analysis at high risk of bias | PROBAST step 3, risk of bias domain 4 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 4 | ||
| Case‐mix | Age | Mean | ||
| SD | ||||
| Median | ||||
| IQR 25th percentile | ||||
| IQR 75th percentile | ||||
| Other | ||||
| Sex | % of men | |||
| Baseline complaints about the side effects | If the outcome of the study is dysphagia, report the proportion of patients who complained about dysphagia at baseline. | |||
| Type of cancer | % of oral cavity cancer | |||
| % of larynx cancer | ||||
| % of hypopharynx cancer | ||||
| % of oropharynx cancer | ||||
| % of nasopharynx cancer | ||||
| % of others | ||||
| Stage | % of stage I | |||
| % of stage II | ||||
| % of stage III | ||||
| % of stage IV | ||||
| Type of radiotherapy | % of IMRT | |||
| % of VMAT | ||||
| % of 3D conformal | ||||
| % of brachytherapy | ||||
| % of proton therapy | ||||
| Radiation target dose (Gy) | ||||
| Radiation fractionation ‐ Conventional fractionation ‐ Hypofractionation ‐ Hyperfractionation ‐ Accelerated ‐ Accelerated hyperfractionation ‐ Others (specify) | ||||
| Treatment | % of concurrent chemotherapy | |||
| % of induction chemotherapy | ||||
| % of adjuvant chemotherapy | ||||
| % of concurrent molecular‐targeted therapy | ||||
| Model performance | Apparent performance | Apparent performance was reported? | Yes or no | |
| C‐statistics | ||||
| C‐statistics SE | ||||
| C‐statistics LCI | ||||
| C‐statistics UCI | ||||
| Calibration plot | If yes, which figure? | |||
| Other | Brier score, O:E ratio, etc… Describe as point estimate (SE) or point estimate (95% CI) as possible. | |||
| Internal validation | Internal validation was performed? | Yes or no If no, move to the section of comment | ||
| C‐statistics | ||||
| C‐statistics SE | ||||
| C‐statistics LCI | ||||
| C‐statistics UCI | ||||
| Calibration plot | If yes, which figure? | |||
| Other | Brier score, O:E ratio, etc… Describe as point estimate (SE) or point estimate (95% CI) as possible | |||
| Comments | Any other comments? | |||
| Abbreviations: AIC: Akaike information criteria BW: backward CI: confidence interval EPV: event per variable FW: forward IMRT: intensity modulated radiotherapy IQR: interquartile range LCI: lower confidence interval NA: not applicable NR: not reported RCT: randomised controlled trial SCC: squamous cell cancer SD: standard deviation SE: standard error TNM: tumour, nodes, metastasis UCI: upper confidence interval VMAT: volumetric modulated arc therapy | ||||
Appendix 3. Preliminary study selection, data extraction and risk of bias forms for external validation studies
| Domain | Item to answer | Explanation/Classification | Reference | |
| General information | Data extracted by | Your name | ||
| First author | Surname of the 1st author | |||
| Year of publication | yyyy | |||
| Sub ID | If the study reports multiple models (e.g. different outcomes, different presentations, etc.), enter the items in different rows. For example, if there are two models for 30‐day and 6‐month mortality, label them as "1st author's surname‐1" and "1st author's surname‐2". Also, if the model was externally validated, enter the information about the external validation in another row. | |||
| Sub ID details | Describe the details of the models to distinguish them from each other. (For example, for the left cell, describe as "model for predicting 30‐day mortality" and "external validation", respectively) | |||
| Study characteristics | Study description | Validation model | Provide the complete reference of the original study from which the validated model was developed. Authors, title, journal, year, volume, issue, pages | CHARMS domain 1: Source of data |
| Indicate which version of the model was validated | ‐ Original model with regression coefficients ‐ Simplified model based on a risk score ‐ Others (specify) | |||
| Number of centres | 1, 2, 3... | |||
| Location | ‐ North America ‐ Europe ‐ Asia ‐ Australia ‐ Africa ‐ South America ‐ Central America ‐ Combination | |||
| Participants | Study description | Data source | ‐ Prospective cohort ‐ Retrospective cohort ‐ Nested case‐control ‐ Non‐nested case‐control ‐ Case‐cohort ‐ RCT ‐ Registry data ‐ Others (specify) ‐ Unclear | CHARMS domain 2: Participants |
| Recruitment method | Were patients recruited consecutively? | |||
| Start date recruitment period | dd‐mm‐yyyy If the exact date is not reported (e.g. only year is reported), just enter available information (e.g. yyyy) | |||
| End date of recruitment period | dd‐mm‐yyyy If the exact date is not reported (e.g. only year is reported), just enter available information (e.g. yyyy). | |||
| Inclusion criteria | Copy and paste from the article text | |||
| Exclusion criteria | Copy and paste from the article text | |||
| Primary site of cancer | Primary site ‐ Oral cavity ‐ Larynx ‐ Hypopharynx ‐ Oropharynx ‐ Nasopharynx ‐ Salivary glands ‐ Nasal cavity ‐ Paranasal sinus ‐ Eye ‐ Lymphoma ‐ Soft tissue ‐ Skull base ‐ Ear ‐ Lip ‐ Thyroid ‐ Others (specify) | |||
| Type of cancer | Pathology of cancer ‐ SCC ‐ Adenocarcinoma ‐ Melanoma ‐ Sarcoma ‐ Lymphoma ‐ Others (specify) ‐ Unclear | |||
| TNM classification | Which version of the classification was used? ‐ 7th version (before 2017) ‐ 8th version (after 2017) | |||
| Stage | Based on TNM classification (I, II, III, IV…) | |||
| Type of radiotherapy | ‐ IMRT ‐ VMAT ‐ 3D conformal ‐ Proton ‐ Electron ‐ Combined ‐ Others (specify) ‐ Unclear | |||
| Surgery | ‐ No surgery ‐ Pre‐surgery radiotherapy ‐ Post‐surgery radiotherapy ‐ Non‐specified | |||
| Type of surgery if specified | Detailed information about surgery (e.g. laryngectomy) Copy and paste from the paper | |||
| Chemotherapy | At baseline: ‐ No chemotherapy ‐ Concurrent chemotherapy ‐ Chemotherapy before radiotherapy ‐ Chemotherapy after radiotherapy ‐ Case mix with different radiotherapy and chemotherapy combinations ‐ Non‐specified ‐ Others (specify) | |||
| Molecular‐targeted therapy | At baseline: ‐ No molecular‐targeted therapy ‐ Concurrent molecular‐targeted therapy ‐ Molecular‐targeted therapy before radiotherapy ‐ Molecular‐targeted therapy after radiotherapy ‐ Case mix with different radiotherapy and molecular therapy combinations ‐ Non‐specified ‐ Others (specify) | |||
| Other specific patient characteristics | ||||
| Signalling questions | Were appropriate data sources used, e.g. cohort, RCT, or nested case‐control study data? | ‐ Yes/probably yes: If a cohort design (including RCT or proper registry data) or a nested case‐control or case‐cohort design (with proper adjustment of the baseline risk/hazard in the analysis) has been used ‐ No/probably no: If a non‐nested case‐control design has been used ‐ No information: If the method of participant sampling is unclear | PROBAST step 3, question 1.1 | |
| Were all inclusions and exclusions of participants appropriate? | ‐ Yes/probably yes: If inclusion and exclusion of participants are appropriate, so participants corresponded to unselected participants of interest ‐ No/probably no: If participants are included who would already have been identified as having the outcome and so are no longer participants at risk of developing outcome, or if specific subgroups are excluded that may have altered the performance of the prediction model for the intended target population ‐ No information: When there is no information on whether inappropriate inclusions or exclusions took place | PROBAST step 3, question 1.2 | ||
| Risk of bias | Risk of bias introduced by participants or data sources | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be "Low risk of bias", but specific reasons should be provided why the risk of bias can be considered low ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias, except if defined as being at low risk of bias above ‐ Unclear risk of bias: If relevant information is missing for some of the signalling questions and none of the signalling questions are judged to put this domain at high risk of bias | PROBAST step 3, risk of bias domain 1 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 1 | ||
| Applicability | Concerns that the included participants or the setting do not match the review question | ‐ Low concerns about applicability: Included participants and clinical setting match the review question ‐ High concerns about applicability: Included participants and clinical setting are different from the review question ‐ Unclear concerns about applicability: If relevant information about the participants and clinical setting is not reported | PROBAST step 3, applicability domain 1 | |
| Rationale of applicability rating | Provide specific reasons for classification of applicability if necessary | PROBAST step 3, applicability domain 1 | ||
| Predictors | Study descriptions | List of predictors included in the final model | A; B; C; … (separate each by ;) | CHARMS domain 4: Candidate predictors |
| Additional degrees of freedom of predictors | If a categorical variable has more than 2 categories, count all extra categories above 2. So, for 3 categories, count 1 (3‐2), for 5 categories, count 3 (5‐2). Do this for all categorical variables and add numbers. Add the number of interaction terms, and the number of degrees of freedom spent modelling non‐linearities | |||
| Predictors | Signalling questions | Were predictors defined and assessed in a similar way for all participants? | ‐ Yes/probably yes: If definitions of predictors and their assessment were similar for all participants ‐ No/probably no: If different definitions were used for the same predictor or if predictors requiring subjective interpretation were assessed by differently experienced assessors ‐ No information: If there is no information on how predictors were defined or assessed | PROBAST step 3, question 2.1 |
| Were predictor assessments made without knowledge of outcome data? | ‐ Yes/probably yes: If outcome information was stated as not used during predictor assessment or was clearly not (yet) available to those assessing predictors ‐ No/probably no: If it is clear that outcome information was used when assessing predictors ‐ No information: No information on whether predictors were assessed without knowledge of outcome information | PROBAST step 3, question 2.2 | ||
| Are all predictors available at the time the model is intended to be used? | ‐ Yes/probably yes: All included predictors would be available at the time the model is intended to be used for prediction ‐ No/probably no: Predictors would not be available at the time the model is intended to be used for prediction ‐ No information: No information on whether predictors would be available at the time the model is intended to be used for prediction | PROBAST step 3, question 2.3 | ||
| Risk of bias | Risk of bias introduced by predictors or their assessment | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be "Low risk of bias" but specific reasons should be provided why the risk of bias can be considered low, e.g. use of objective predictors not requiring subjective interpretation ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias ‐ Unclear risk of bias: If relevant information is missing for some of the signalling questions and none of the signalling questions is judged to put the domain at high risk of bias | PROBAST step 3, risk of bias domain 2 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 2 | ||
| Applicability | Concerns that the definition, assessment, or timing of predictors in the model do not match the review question | ‐ Low concerns about applicability: Definition, assessment, and timing of predictors match the review question ‐ High concerns about applicability: Definition, assessment, or timing of predictors are different from the review question ‐ Unclear concerns about applicability: If relevant information about the predictors is not reported | PROBAST step 3, applicability domain 2 | |
| Rationale of applicability rating | Provide specific reasons for classification of applicability if necessary | PROBAST step 3, applicability domain 1 | ||
| Outcome | Outcome | ‐ Oral mucositis ‐ Tube‐feeding dependence ‐ Xerostomia ‐ Disgeusia ‐ Dysphagia ‐ Oesophagitis ‐ Hypothyroidism ‐ Hearing loss ‐ Tinnitus ‐ Visual loss ‐ Wound healing complications ‐ Aspiration ‐ Dermatitis ‐ Head and neck pain ‐ Hoarseness ‐ Oral pain ‐ Salivary duct inflammation ‐ Sore throat ‐ Weight loss ‐ Fatigue ‐ Osteoradionecrosis ‐ Neurocognitive dysfunction ‐ Cerebrovascular events ‐ Speech problems ‐ Jaw pain ‐ Nausea and vomiting ‐ Others (specify) | CHARMS domain 3: Outcome | |
| Definition | Describe the definition of the outcome (copy and paste from the original article) | |||
| Time point of the outcome | Days/months/years | |||
| Signalling questions | Was the outcome determined appropriately? | ‐ Yes/probably yes: If a method of outcome determination has been used which is considered optimal or acceptable by guidelines or previous publications on the topic. Note: this is about the level of measurement error within the method of determining the outcome (see concerns for applicability about whether the definition of the outcome method is appropriate) ‐ No/probably no: If a clearly suboptimal method has been used that causes unacceptable errors in determining outcome status in participants ‐ No information: No information on how the outcome is determined | PROBAST step 3, question 3.1 | |
| Was a prespecified or standard outcome definition used? | ‐ Yes/probably yes: If the method of outcome determination is objective, or if a standard outcome definition is used, or if prespecified categories are used to group outcomes ‐ No/probably no: If the outcome definition is not standard and not prespecified ‐ No information: No information on whether the outcome definition is prespecified or standard | PROBAST step 3, question 3.2 | ||
| Were predictors excluded from the outcome definition? | ‐ Yes/probably Yes: If none of the predictors are included in the outcome definition ‐ No/probably No: If ≥ 1 of the predictors forms part of the outcome definition ‐ No information: No information on whether predictors are excluded from the outcome definition | PROBAST step 3, question 3.3 | ||
| Was the outcome defined and determined in a similar way for all participants? | ‐ Yes/probably yes: If outcomes are defined and determined in a similar way for all participants ‐No/probably no: If outcomes are clearly defined and determined in a different way for some participants ‐ No information: No information on whether predictors are excluded from the outcome definition | PROBAST step 3, question 3.4 | ||
| Was the outcome determined without knowledge of predictor information? | ‐ Yes/probably yes: If predictor information is not known when determining the outcome status, or outcome status determination is clearly reported as determined without knowledge of predictor information ‐ No/probably no: If it is clear that predictor information is used when determining the outcome status ‐ No information: No information on whether the outcome is determined without knowledge of predictor information | PROBAST step 3, question 3.5 | ||
| Was the time interval between predictor assessment and outcome determination appropriate? | ‐ Yes/probably yes: If the time interval between predictor assessment and outcome determination is appropriate to enable the correct type and representative number of relevant outcomes to be recorded, or if no information on the time interval is required to allow a representative number of the relevant outcomes to occur or if predictor assessment and outcome determination are from information taken within an appropriate time interval ‐ No/probably no: If the time interval between predictor assessment and outcome determination is too short or too long to enable the correct type and representative number of relevant outcomes to be recorded ‐ No information: If no information is provided on the time interval between predictor assessment and outcome determination | PROBAST step 3, question 3.6 | ||
| Risk of bias | Risk of bias introduced by the outcome or its determination | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be low risk of bias, but specific reasons should be provided why the risk of bias can be considered low, e.g. when the outcome is determined with knowledge of predictor information, but the outcome assessment does not require much interpretation by the assessor (e.g. death regardless of cause) ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias ‐ Unclear risk of bias: If relevant information about the outcome is missing for some of the signalling questions and none of the signalling questions is judged to put this domain at high risk of bias | PROBAST step 3, risk of bias domain 3 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 3 | ||
| Applicability | Concern that the outcome definition, timing, or determination do not match the review question | ‐ Low concerns about applicability: Outcome definition, timing, and method of determination define the outcome as intended by the review question ‐ High concerns about applicability: Choice of outcome definition, timing, and method of outcome determination defines another outcome as intended by the review question ‐ Unclear concerns about applicability: If relevant information about the outcome, timing, and method of determination is not reported | PROBAST step 3, applicability domain 3 | |
| Rationale of applicability rating | Provide specific reasons for classification of applicability if necessary | PROBAST step 3, applicability domain 1 | ||
| Analyses | Number of participants | Specify the number of participants included in the analysis | CHARMS domain 5: Sample size | |
| Number of participants with the outcome | Specify the number of participants with the outcome | CHARMS domain 5: Sample size | ||
| Handling of missing data | How were missing data handled in the analysis? ‐ Complete‐case analysis ‐ Single imputation ‐ Multiple imputations ‐ Sensitivity analysis ‐ Variable omission ‐ Others (specify) ‐ Not reported | CHARMS domain 6: Missing data | ||
| Number of participants with any missing predictor/outcome values | Specify number. When missing data are exclusion criteria, add 0. If not reported, write "NR" | |||
| Model updated? | Was the model updated? ‐ Yes ‐ No | CHARMS domain 9: Model evaluation | ||
| The method for updating | If yes, which method was used to update the model? ‐ None ‐ Recalibration‐in‐the‐large ‐ Recalibration ‐ Complete model revision | |||
| Signalling questions | Were there a reasonable number of participants with the outcome? | ‐ Yes/probably yes: For model validation studies, if the number of participants with the outcome is ≥ 100 ‐ No/probably no: For model validation studies, if the number of participants with the outcome is < 100 ‐ No information: For model validation studies, no information on the number of participants with the outcome | PROBAST step 3, question 4.1 | |
| Were all enroled participants included in the analysis? | ‐ Yes/probably yes: If all participants enroled in the study are included in the data analysis. ‐ No/probably no: If some or a subgroup of participants are inappropriately excluded from the analysis ‐ No information: No information on whether all enroled participants are included in the analysis | PROBAST step 3, question 4.3 | ||
| Were participants with missing data handled appropriately? | ‐ Yes/probably yes:If there are no missing values of predictors or outcomes and the study explicitly reports that participants are not excluded on the basis of missing data, or if missing values are handled using multiple imputations ‐ No/probably no: If participants with missing data are omitted from the analysis, or if the method of handling missing data is clearly flawed, e.g. missing indicator method or inappropriate use of last value carried forward, or if the study has no explicit mention of methods to handle missing data ‐ No information: If there is insufficient information to determine if the method of handling missing data is appropriate | PROBAST step 3, question 4.4 | ||
| Were relevant model performance measures evaluated appropriately? | ‐ Yes/probably yes: If both calibration and discrimination are evaluated appropriately (including relevant measures tailored for models predicting survival outcomes) ‐ No/probably no: If both calibration and discrimination are not evaluated, or if only goodness‐of‐fit tests, such as the Hosmer‐Lemeshow test, are used to evaluate calibration, or if models predicting survival outcomes performance measures accounting for censoring ae not used, or if classification measures (like sensitivity, specificity, or predictive values) are presented using predicted probability thresholds derived from the dataset at hand ‐ No information: Either calibration or discrimination are not reported, or no information is provided on whether appropriate performance measures for survival outcomes are used (e.g. references to relevant literature or specific mention of methods, such as using Kaplan‐Meier estimates), or no information on thresholds for estimating classification measures is given | PROBAST step 3, question 4.7 | ||
| Do predictors and their assigned weights in the final model correspond to the results from the reported multivariable analysis? | ‐ Yes/probably yes: If the predictors and regression coefficients in the final model correspond to reported results from multivariable analysis ‐ No/probably no: If the predictors and regression coefficients in the final model do not correspond to reported results from multivariable analysis ‐ No information: If it is unclear whether the regression coefficients in the final model correspond to reported results from multivariable analysis | PROBAST step 3, question 4.9 | ||
| Risk of bias | Risk of bias introduced by the analysis | ‐ Low risk of bias: If the answer to all signalling questions is "Yes" or "Probably yes", then the risk of bias can be considered low. If ≥ 1 of the answers is "No" or "Probably no", the judgement could still be low risk of bias, but specific reasons should be provided why the risk of bias can be considered low ‐ High risk of bias: If the answer to any of the signalling questions is "No" or "Probably no", there is a potential for bias ‐ Unclear risk of bias: If relevant information about the analysis is missing for some of the signalling questions, but none of the signalling questions is judged to put the analysis at high risk of bias | PROBAST step 3, risk of bias domain 4 | |
| Rationale of bias rating | Provide specific reasons for classification of bias if necessary | PROBAST step 3, risk of bias domain 4 | ||
| Case‐mix | Age | Mean | ||
| SD | ||||
| Median | ||||
| IQR 25th percentile | ||||
| IQR 75th percentile | ||||
| Other | ||||
| Sex | % of men | |||
| Baseline complaints about the side effects | If the outcome of the study is dysphagia, report the proportion of patients who complained about dysphagia at baseline | |||
| Type of cancer | % of oral cavity cancer | |||
| % of larynx cancer | ||||
| % of hypopharynx cancer | ||||
| % of oropharynx cancer | ||||
| % of nasopharynx cancer | ||||
| % of others | ||||
| Stage | % of stage I | |||
| % of stage II | ||||
| % of stage III | ||||
| % of stage IV | ||||
| Type of radiotherapy | % of IMRT | |||
| % of VMAT | ||||
| % of 3D conformal | ||||
| % of brachytherapy | ||||
| % of proton therapy | ||||
| Radiation target dose (Gy) | ||||
| Radiation fractionation ‐ Conventional fractionation ‐ Hypofractionation ‐ Hyperfractionation ‐ Accelerated ‐ Accelerated hyperfractionation ‐ Others (specify) | ||||
| Treatment | % of concurrent chemotherapy | |||
| % of induction chemotherapy | ||||
| % of adjuvant chemotherapy | ||||
| % of concurrent molecular‐targeted therapy | ||||
| Model performance | Model performance without update | Model performance with update reported? | Yes or No | |
| C‐statistics | ||||
| C‐statistics SE | ||||
| C‐statistics LCI | ||||
| C‐statistics UCI | ||||
| Calibration plot | If yes, which figure? | |||
| Other | ||||
| Model performance with update | Model performance with update reported? | Yes or No | ||
| C‐statistics | ||||
| C‐statistics SE | ||||
| C‐statistics LCI | ||||
| C‐statistics UCI | ||||
| Calibration plot | If yes, which figure? | |||
| Other | ||||
| Comments | Any other comments? | |||
| Abbreviations: IMRT: intensity modulated radiotherapy IQR: interquartile range LCI: lower confidence interval RCT: randomised controlled trial SCC: squamous cell carcinoma SD: standard deviation SE: standard error TNM: tumour, nodes, metastasis UCI: upper confidence interval VMAT: volumetric modulated arc therapy | ||||
Appendix 4. Missing information in models with at least 2 external validations
| Model | Number of cohorts | Recruitment period reported in | Study design reported in | Timing of outcome reported in | Total sample size reported in | Number of events reported in | Calibration reported in |
| Beetz (2012b)‐1 | 3 | 2 | 3 | 3 | 3 | 3 | 2 |
| Cavallo (2021)‐1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| Christianen (2012)‐1 | 6 | 4 | 6 | 6 | 6 | 6 | 4 |
| Wopken (2014b)‐1 | 4 | 2 | 3 | 4 | 4 | 4 | 3 |
| Boomsma (2012)‐1 | 2 | 2 | 2 | 2 | 2 | 2 | 1 |
| Ronjom (2013)‐1 | 3 | 3 | 3 | 2 | 3 | 3 | 1 |
| Cella (2012)‐2 | 2 | 2 | 2 | 1 | 2 | 2 | 0 |
| OuYang (2023)‐1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
| Wen (2021)‐1 | 6 | 6 | 6 | 6 | 6 | 6 | 0 |
| Yang (2023a)‐1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
Appendix 5. Risk of bias assessment of developed models (n = 617) from 152 publications
| Publication | Study subID | PROBAST ‐ Risk of Bias | ||||
| Participants Domain | Predictors Domain | Outcome Domain | Analysis Domain | Overall Judgement | ||
| Abdollahi 2023 | Abdollahi (2023)‐1 | Low | Low | Unclear | High | High |
| Abdollahi (2023)‐2 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐3 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐4 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐5 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐6 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐7 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐8 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐9 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐10 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐11 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐12 | Low | Low | Unclear | High | High | |
| Abdollahi (2023)‐13 | Low | Low | Unclear | High | High | |
| Alterio 2017 | Alterio (2017)‐1 | Low | Low | Unclear | High | High |
| Anderson 2018 | Anderson (2018)‐1 | Low | Low | Low | High | High |
| Anderson (2018)‐2 | Low | Low | Low | High | High | |
| Bakhshandeh 2013 | Bakhshandeh (2012)‐1 | Low | Low | Low | Unclear | Unclear |
| Bakhshandeh (2012)‐2 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐3 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐4 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐5 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐6 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐7 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐8 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐9 | Low | Low | Low | Unclear | Unclear | |
| Bakhshandeh (2012)‐10 | Low | Low | Low | Unclear | Unclear | |
| Beetz 2012a | Beetz (2012a)‐1 | Low | Low | Low | High | High |
| Beetz (2012a)‐2 | Low | Low | Low | High | High | |
| Beetz 2012b | Beetz (2012b)‐1 | Low | Low | Low | High | High |
| Beetz (2012b)‐2 | Low | Low | Low | High | High | |
| Bhide 2012 | Bhide (2012)‐1 | Unclear | Low | Low | High | High |
| Bhide (2012)‐2 | Unclear | Low | Low | High | High | |
| Bin 2022 | Bin (2022)‐1 | Low | Low | Low | High | High |
| Blanco 2005 | Blanco (2005)‐1 | Unclear | Low | Low | High | High |
| Blanco (2005)‐2 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐3 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐4 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐5 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐6 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐7 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐8 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐9 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐10 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐11 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐12 | Unclear | Low | Low | High | High | |
| Blanco (2005)‐13 | Unclear | Low | Low | High | High | |
| Boomsma 2012 | Boomsma (2012)‐1 | Low | Low | Low | High | High |
| Buettner 2010 | Buettner (2010)‐1 | Low | Low | Low | High | High |
| Buettner (2010)‐2 | Low | Low | Low | High | High | |
| Buettner (2010)‐3 | Low | Low | Low | High | High | |
| Buettner (2010)‐4 | Low | Low | Low | High | High | |
| Buettner (2010)‐5 | Low | Low | Low | High | High | |
| Buettner (2010)‐6 | Low | Low | Low | High | High | |
| Buettner (2010)‐7 | Low | Low | Low | High | High | |
| Buettner (2010)‐8 | Low | Low | Low | High | High | |
| Buettner (2010)‐9 | Low | Low | Low | High | High | |
| Buettner (2010)‐10 | Low | Low | Low | High | High | |
| Buettner (2010)‐11 | Low | Low | Low | High | High | |
| Busato 2023 | Busato (2023)‐1 | Low | Low | Low | High | High |
| Busato (2023)‐2 | Low | Low | Low | High | High | |
| Busato (2023)‐3 | Low | Low | Low | High | High | |
| Busato (2023)‐4 | Low | Low | Low | High | High | |
| Busato (2023)‐5 | Low | Low | Low | High | High | |
| Busato (2023)‐6 | Low | Low | Low | High | High | |
| Cavallo 2021 | Cavallo (2021)‐1 | High | Low | Low | High | High |
| Chao 2001 | Chao (2001)‐1 | Low | Low | Low | High | High |
| Chao (2001)‐2 | Low | Low | Low | High | High | |
| Chao (2001)‐3 | Low | Low | Low | High | High | |
| Chao (2001)‐4 | Low | Low | Low | High | High | |
| Chao (2001)‐5 | Low | Low | Low | High | High | |
| Chao (2001)‐6 | Low | Low | Low | High | High | |
| Chao (2001)‐7 | Low | Low | Low | High | High | |
| Chao (2001)‐8 | Low | Low | Low | High | High | |
| Chao (2001)‐9 | Low | Low | Low | High | High | |
| Chao (2001)‐10 | Low | Low | Low | High | High | |
| Chao 2022 | Chao (2022)‐1 | Unclear | Low | Low | High | High |
| Chao (2022)‐2 | Unclear | Low | Low | High | High | |
| Chao (2022)‐3 | Unclear | Low | Low | High | High | |
| Chao (2022)‐4 | Unclear | Low | Low | High | High | |
| Chen 2013 | Chen (2013)‐1 | Low | Low | Low | High | High |
| Chen (2013)‐2 | Low | Low | Low | High | High | |
| Cheng 2018 | Cheng (2018)‐1 | Unclear | Low | Low | High | High |
| Cheng 2019 | Cheng (2019)‐1 | Unclear | Low | Low | High | High |
| Cheraghi 2017 | Cheraghi (2017)‐1 | Low | Low | Low | High | High |
| Cheraghi (2017)‐2 | Low | Low | Low | High | High | |
| Cheraghi (2017)‐3 | Low | Low | Low | High | High | |
| Cheraghi (2017)‐4 | Low | Low | Low | High | High | |
| Cheraghi (2017)‐5 | Low | Low | Low | High | High | |
| Cheraghi (2017)‐6 | Low | Low | Low | High | High | |
| Chow 2019 | Chow (2019)‐1 | High | Low | High | High | High |
| Christianen 2012 | Christianen (2012)‐1 | Low | Low | Low | Unclear | Unclear |
| Christianen (2012)‐2 | Low | Low | Low | Unclear | Unclear | |
| Christianen (2012)‐3 | Low | Low | Low | Unclear | Unclear | |
| Christianen (2012)‐4 | Low | Low | Low | Unclear | Unclear | |
| Christianen (2012)‐5 | Low | Low | Low | Unclear | Unclear | |
| Dale 2016 | Dale (2016)‐1 | High | Low | High | High | High |
| Dale (2016)‐2 | High | Low | High | High | High | |
| Dean 2016 | Dean (2016)‐1 | Low | Low | Low | High | High |
| Dean (2016)‐2 | Low | Low | Low | High | High | |
| Dean (2016)‐3 | Low | Low | Low | High | High | |
| Dean (2016)‐4 | Low | Low | Low | High | High | |
| Dean (2016)‐5 | Low | Low | Low | High | High | |
| Dean (2016)‐6 | Low | Low | Low | High | High | |
| Dean 2018 | Dean (2018)‐1 | Low | Low | Low | High | High |
| Dean (2018)‐2 | Low | Low | Low | High | High | |
| Dean (2018)‐3 | Low | Low | Low | High | High | |
| Dean (2018)‐4 | Low | Low | Low | High | High | |
| Dean (2018)‐5 | Low | Low | Low | High | High | |
| Dean (2018)‐6 | Low | Low | Low | High | High | |
| Deneuve 2023 | Deneuve (2023)‐1 | Unclear | Low | Low | High | High |
| Deneuve (2023)‐2 | Unclear | Low | Low | High | High | |
| Deneuve (2023)‐3 | Unclear | Low | Low | High | High | |
| Deneuve (2023)‐4 | Unclear | Low | Low | High | High | |
| Dijkema 2010 | Dijkema (2010)‐1 | Unclear | Low | Low | High | High |
| Dijkema (2010)‐2 | Unclear | Low | Low | High | High | |
| Dijkema (2010)‐3 | Unclear | Low | Low | High | High | |
| Dijkema (2010)‐4 | Unclear | Low | Low | High | High | |
| Dohopolski 2022 | Dohopolski (2022)‐1 | Unclear | Low | Low | High | High |
| Dohopolski (2022)‐2 | Unclear | Low | Low | High | High | |
| Dohopolski (2022)‐3 | Unclear | Low | Low | High | High | |
| Dong 2023 | Dong (2023)‐1 | Low | Low | Unclear | High | High |
| Dong (2023)‐2 | Low | Low | Unclear | High | High | |
| Dong (2023)‐3 | Low | Low | Unclear | High | High | |
| Eisbruch 1999 | Eisbruch (1999)‐1 | Unclear | Low | Low | High | High |
| Eisbruch (1999)‐2 | Unclear | Low | Low | High | High | |
| Eisbruch (1999)‐3 | Unclear | Low | Low | High | High | |
| Eisbruch 2011 | Eisbruch (2011)‐1 | Unclear | Low | Low | High | High |
| Eisbruch (2011)‐2 | Unclear | Low | Low | High | High | |
| Eisbruch (2011)‐3 | Unclear | Low | Low | High | High | |
| Eisbruch (2011)‐4 | Unclear | Low | Low | High | High | |
| Eisbruch (2011)‐5 | Unclear | Low | Low | High | High | |
| Eisbruch (2011)‐6 | Unclear | Low | Low | High | High | |
| Eisbruch (2011)‐7 | Unclear | Low | Low | High | High | |
| Eisbruch (2011)‐8 | Unclear | Low | Low | High | High | |
| Fanizzi 2022 | Fanizzi (2022)‐1 | Low | Low | Unclear | High | High |
| Fanizzi (2022)‐2 | Low | Low | Unclear | High | High | |
| Fanizzi (2022)‐3 | Low | Low | Unclear | High | High | |
| Fanizzi (2022)‐4 | Low | Low | Unclear | High | High | |
| Guan 2020 | Guan (2020)‐1 | Low | Low | Low | High | High |
| Gupta 2015 | Gupta (2015)‐1 | Unclear | Low | Low | High | High |
| Gupta (2015)‐2 | Unclear | Low | Low | High | High | |
| Gupta (2015)‐3 | Unclear | Low | Low | High | High | |
| Gupta (2015)‐4 | Unclear | Low | Low | High | High | |
| Hamada 2023 | Hamada (2023)‐1 | Low | Low | Low | Low | Low |
| Hansen 2020 | Hansen (2020)‐1 | Low | Low | Low | Low | Low |
| Hansen (2020)‐2 | Low | Low | Low | Low | Low | |
| He 2023 | He (2023)‐1 | Low | Low | High | High | High |
| He (2023)‐2 | Low | Low | High | High | High | |
| He (2023)‐3 | Low | Low | High | High | High | |
| He 2024 | He (2024)‐1 | Low | Low | Low | High | High |
| Hosseinian 2023 | Hosseinian (2023)‐1 | Low | Low | Low | High | High |
| Houweling 2010 | Houweling (2010)‐1 | Low | Low | Low | High | High |
| Houweling (2010)‐2 | Low | Low | Low | High | High | |
| Houweling (2010)‐3 | Low | Low | Low | High | High | |
| Houweling (2010)‐4 | Low | Low | Low | High | High | |
| Houweling (2010)‐5 | Low | Low | Low | High | High | |
| Houweling (2010)‐6 | Low | Low | Low | High | High | |
| Huang 2022 | Huang (2022)‐1 | Low | Low | Low | High | High |
| Humbert‐Vidan 2021 | Humbert‐Vidan (2021)‐1 | High | High | Low | High | High |
| Humbert‐Vidan (2021)‐2 | High | High | Low | High | High | |
| Humbert‐Vidan (2021)‐3 | High | High | Low | High | High | |
| Humbert‐Vidan (2021)‐4 | High | High | Low | High | High | |
| Humbert‐Vidan (2021)‐5 | High | High | Low | High | High | |
| Humbert‐Vidan 2022 | Humbert‐Vidan (2022)‐1 | Low | Low | Low | High | High |
| Humbert‐Vidan (2022)‐2 | Low | Low | Low | High | High | |
| Kamstra 2015 | Kamstra (2015)‐1 | Low | Low | Low | Unclear | Unclear |
| Kamstra (2015)‐2 | Low | Low | Low | High | High | |
| Kanehira 2021 | Kanehira (2021)‐1 | Low | Low | Low | High | High |
| Kanehira (2021)‐2 | Low | Low | Low | High | High | |
| Kanehira (2021)‐3 | Low | Low | Low | High | High | |
| Kanehira (2021)‐4 | Low | Low | Low | High | High | |
| Kanehira (2021)‐5 | Low | Low | Low | High | High | |
| Kanehira (2021)‐6 | Low | Low | Low | High | High | |
| Kanehira (2021)‐7 | Low | Low | Low | High | High | |
| Karsten 2019 | Karsten (2019)‐1 | High | Low | Low | Unclear | High |
| Kawamura 2019 | Kawamura (2019)‐1 | High | Low | Low | High | High |
| Kraaijenga 2019 | Kraaijenga (2019)‐1 | Low | Low | Low | High | High |
| Kraaijenga (2019)‐2 | Low | Low | Low | High | High | |
| Krasin 2012 | Krasin (2012)‐1 | Unclear | Low | Low | High | High |
| Krasin (2012)‐2 | Unclear | Low | Low | High | High | |
| Krasin (2012)‐3 | Unclear | Low | Low | High | High | |
| Langendijk 2009 | Langendijk (2009)‐1 | Low | Low | Low | High | High |
| Langius 2016 | Langius (2016)‐1 | Low | Low | Low | High | High |
| Langius (2016)‐2 | Low | Low | Low | High | High | |
| Lee 2012 | Lee (2012)‐1 | Low | Low | Low | High | High |
| Lee (2012)‐2 | Low | Low | Low | High | High | |
| Lee 2014a | Lee (2014a)‐1 | Unclear | Low | Low | High | High |
| Lee (2014a)‐2 | Unclear | Low | Low | High | High | |
| Lee 2014b | Lee (2014b)‐1 | Low | Low | Low | High | High |
| Lee (2014b)‐2 | Low | Low | Low | High | High | |
| Lee (2014b)‐3 | Low | Low | Low | High | High | |
| Lee (2014b)‐4 | Low | Low | Low | High | High | |
| Lee (2014b)‐5 | Low | Low | Low | High | High | |
| Lee (2014b)‐6 | Low | Low | Low | High | High | |
| Lee (2014b)‐7 | Low | Low | Low | High | High | |
| Lee (2014b)‐8 | Low | Low | Low | High | High | |
| Lee (2014b)‐9 | Low | Low | Low | High | High | |
| Lee (2014b)‐10 | Low | Low | Low | High | High | |
| Lee (2014b)‐11 | Low | Low | Low | High | High | |
| Lee (2014b)‐12 | Low | Low | Low | High | High | |
| Lee 2015a | Lee (2015a)‐1 | High | Low | Low | Unclear | High |
| Lee (2015a)‐2 | High | Low | Low | Unclear | High | |
| Lee 2015b | Lee (2015b)‐1 | Low | Low | Low | High | High |
| Lee (2015b)‐2 | Low | Low | Low | High | High | |
| Lee (2015b)‐3 | Low | Low | Low | High | High | |
| Lee (2015b)‐4 | Low | Low | Low | High | High | |
| Lee (2015b)‐5 | Low | Low | Low | High | High | |
| Lee (2015b)‐6 | Low | Low | Low | High | High | |
| Lee 2023 | Lee (2023)‐1 | Low | Low | Low | High | High |
| Lee (2023)‐2 | Low | Low | Low | High | High | |
| Lee (2023)‐3 | Low | Low | Low | High | High | |
| Lescut 2013 | Lescut (2013)‐1 | Low | High | High | High | High |
| Lescut (2013)‐2 | Low | High | High | High | High | |
| Li 2020 | Li (2020)‐1 | Unclear | Low | Low | High | High |
| Li (2020)‐2 | Unclear | Low | Low | High | High | |
| Li 2022a | Li (2022a)‐1 | Low | Low | Low | Unclear | Unclear |
| Li (2022a)‐2 | Low | Low | Low | High | High | |
| Li 2022b | Li (2022b)‐1 | Low | Low | Unclear | High | High |
| Li (2022b)‐2 | Low | Low | Unclear | High | High | |
| Li (2022b)‐3 | Low | Low | Unclear | High | High | |
| Lindblom 2014 | Lindblom (2014)‐1 | Low | Low | Low | Unclear | Unclear |
| Lindblom (2014)‐2 | Low | Low | Low | Unclear | Unclear | |
| Ling 2020 | Ling (2020)‐1 | Low | Low | Low | High | High |
| Ling (2020)‐2 | Low | Low | Low | High | High | |
| Ling (2020)‐3 | Low | Low | Low | High | High | |
| Ling (2020)‐4 | Low | Low | Low | High | High | |
| Liu 2019 | Liu (2019)‐1 | Low | Low | Low | High | High |
| Liu (2019)‐2 | Low | Low | Low | High | High | |
| Liu 2022 | Liu (2022)‐1 | Low | Low | Low | High | High |
| Luo 2017 | Luo (2017)‐1 | Low | Low | Low | High | High |
| Luo 2018 | Luo (2018)‐1 | Low | Low | Low | High | High |
| Marzi 2009 | Marzi (2009)‐1 | Low | Low | Low | High | High |
| Marzi (2009)‐2 | Low | Low | Low | High | High | |
| Marzi (2009)‐3 | Low | Low | Low | High | High | |
| Marzi (2009)‐4 | Low | Low | Low | High | High | |
| Marzi (2009)‐5 | Low | Low | Low | High | High | |
| Marzi (2009)‐6 | Low | Low | Low | High | High | |
| Marzi 2021 | Marzi (2021)‐1 | Low | Low | Low | High | High |
| Mavroidis 2003 | Mavroidis (2003)‐1 | Unclear | Low | Unclear | High | High |
| Mavroidis 2017 | Mavroidis (2017)‐1 | Low | Low | Low | High | High |
| Mavroidis (2017)‐2 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐3 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐4 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐5 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐6 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐7 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐8 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐9 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐10 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐11 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐12 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐13 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐14 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐15 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐16 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐17 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐18 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐19 | Low | Low | Low | High | High | |
| Mavroidis (2017)‐20 | Low | Low | Low | High | High | |
| Mavroidis 2018 | Mavroidis (2018)‐1 | Low | Low | Unclear | High | High |
| Mavroidis (2018)‐2 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐3 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐4 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐5 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐6 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐7 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐8 | Low | Low | Unclear | High | High | |
| Mavroidis (2018)‐9 | Low | Low | Unclear | High | High | |
| Men 2019 | Men (2019)‐1 | Low | Low | Low | High | High |
| Mori 2019 | Mori (2019)‐1 | Low | Low | Low | High | High |
| Mori (2019)‐2 | Low | Low | Low | High | High | |
| Morimoto 2019 | Morimoto (2019)‐1 | Low | Low | Low | High | High |
| Murthy 2018 | Murthy (2018)‐1 | Low | Low | Low | High | High |
| Murthy (2018)‐2 | Low | Low | Low | High | High | |
| Musha 2020 | Musha (2020)‐1 | Unclear | Low | Low | High | High |
| Musha (2020)‐2 | Unclear | Low | Low | High | High | |
| Musha (2020)‐3 | Unclear | Low | Low | High | High | |
| Musha (2020)‐4 | Unclear | Low | Low | High | High | |
| Nourissat 2010 | Nourissat (2010)‐1 | Low | Low | Low | High | High |
| Nourissat (2010)‐2 | Low | Low | Low | High | High | |
| Onjukka 2020 | Onjukka (2020)‐1 | Low | Low | Low | Unclear | Unclear |
| Onjukka (2020)‐2 | Low | Low | Low | Unclear | Unclear | |
| Onjukka (2020)‐3 | Low | Low | Low | Unclear | Unclear | |
| Onjukka (2020)‐4 | Low | Low | Low | Unclear | Unclear | |
| Orlandi 2018 | Orlandi (2018)‐1 | Low | Low | Low | High | High |
| Orlandi (2018)‐2 | Low | Low | Unclear | High | High | |
| OuYang 2023 | OuYang (2023)‐1 | Low | Low | Low | High | High |
| Padannayil 2023 | Padannayil (2023)‐1 | Unclear | Low | Unclear | High | High |
| Padannayil (2023)‐2 | Unclear | Low | Unclear | High | High | |
| Pan 2020a | Pan (2020a)‐1 | Low | Low | Low | High | High |
| Pan 2020b | Pan (2020b)‐1 | Low | Low | Low | High | High |
| Pan (2020b)‐2 | Low | Low | Low | High | High | |
| Peng 2020 | Peng (2020)‐1 | Low | Low | Low | High | High |
| Peng (2020)‐2 | Low | Low | Low | High | High | |
| Peng (2020)‐3 | Low | Low | Low | High | High | |
| Peuker 2022 | Peuker (2022)‐1 | Low | Low | Unclear | High | High |
| Peuker (2022)‐2 | Low | Low | Unclear | High | High | |
| Pota 2017 | Pota (2017)‐1 | Low | Low | Low | High | High |
| Pota (2017)‐2 | Low | Low | Low | High | High | |
| Pota (2017)‐3 | Low | Low | High | High | High | |
| Qin 2023 | Qin (2023)‐1 | Low | Low | Low | High | High |
| Rachi 2023 | Rachi (2023)‐1 | Unclear | Low | High | High | High |
| Rachi (2023)‐2 | Unclear | Low | High | High | High | |
| Rades 2022 | Rades (2022)‐1 | Unclear | Unclear | High | High | High |
| Rancati 2009 | Rancati (2009)‐1 | Low | Low | Low | High | High |
| Rancati (2009)‐2 | Low | Low | Low | High | High | |
| Rao 2016 | Rao (2016)‐1 | Low | Low | Low | High | High |
| Rao (2016)‐2 | Low | Low | Low | High | High | |
| Rao (2016)‐3 | Low | Low | Low | High | High | |
| Ren 2021 | Ren (2021)‐1 | Unclear | Low | Low | High | High |
| Ren (2021)‐2 | Unclear | Low | Low | High | High | |
| Renda 2020 | Renda (2019)‐1 | Low | Low | Unclear | High | High |
| Renda (2019)‐2 | Low | Low | Unclear | High | High | |
| Ritlumlert 2023 | Ritlumlert (2023)‐1 | Low | Low | Unclear | Unclear | Unclear |
| Ritlumlert (2023)‐2 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐3 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐4 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐5 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐6 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐7 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐8 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐9 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐10 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐11 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐12 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐13 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐14 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐15 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐16 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐17 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐18 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐19 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐20 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐21 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐22 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐23 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐24 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐25 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐26 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐27 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐28 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐29 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐30 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐31 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐32 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐33 | Low | Low | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐34 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ritlumlert (2023)‐35 | Low | Unclear | Unclear | Unclear | Unclear | |
| Ronjom 2013 | Ronjom (2013)‐1 | Low | Low | Low | High | High |
| Ronjom (2013)‐2 | Low | Low | Low | High | High | |
| Ronjom 2015 | Ronjom (2015)‐1 | Low | Low | Low | High | High |
| Rosen 2018 | Rosen (2018)‐1 | Low | Low | Low | High | High |
| Rosen (2018)‐2 | Low | Low | Low | High | High | |
| Rosen (2018)‐3 | Low | Low | Low | High | High | |
| Rosen (2018)‐4 | Low | Low | Low | High | High | |
| Rwigema 2019 | Rwigema (2019)‐1 | Low | Low | Low | High | High |
| Rwigema (2019)‐2 | Low | Low | Low | High | High | |
| Rwigema (2019)‐3 | Low | Low | Low | High | High | |
| Rwigema (2019)‐4 | Low | Low | Low | High | High | |
| Rwigema (2019)‐5 | Low | Low | Low | High | High | |
| Schuette 2020 | Schuette (2020)‐1 | Low | Low | Low | High | High |
| Schuette (2020)‐2 | Low | Low | Low | High | High | |
| Scrimger 2004 | Scrimger (2004)‐1 | High | Low | Low | High | High |
| Sheikh 2019 | Sheikh (2019)‐1 | Low | Low | Low | Low | Low |
| Sheikh (2019)‐2 | Low | Low | Low | Low | Low | |
| Sheikh (2019)‐3 | Low | Low | Low | Low | Low | |
| Sheikh (2019)‐4 | Low | Low | Low | Low | Low | |
| Sheikh (2019)‐5 | Low | Low | Low | Low | Low | |
| Sheikh (2019)‐6 | Low | Low | Low | Low | Low | |
| Sheikh (2019)‐7 | Low | Low | Low | Low | Low | |
| Sheikh (2019)‐8 | Low | Low | Low | Low | Low | |
| Shen 2021 | Shen (2021)‐1 | Low | Low | Low | High | High |
| Sheu 2014 | Sheu (2014)‐1 | Low | Low | Low | Unclear | Unclear |
| Shirai 2017 | Shirai (2017)‐1 | Low | Low | Low | High | High |
| Shorter 2017 | Shorter (2017)‐1 | Low | Unclear | Unclear | High | High |
| Sijtsema 2023 | Sijtsema (2023)‐1 | High | Low | Low | High | High |
| Singh 2023 | Singh (2023)‐1 | Unclear | Unclear | Low | High | High |
| Singh (2023)‐2 | Unclear | Unclear | Low | High | High | |
| Singh (2023)‐3 | Unclear | Unclear | Low | High | High | |
| Singh (2023)‐4 | Unclear | Unclear | Low | High | High | |
| Singla 2021 | Singla (2021)‐1 | Low | Low | Low | High | High |
| Soares 2014 | Soares (2014)‐1 | High | Low | High | High | High |
| Soares 2018 | Soares (2018)‐1 | High | Low | Low | High | High |
| Soderstrom 2017 | Soderstrom (2017)‐1 | High | Low | Low | High | High |
| Somay 2023 | Somay (2023)‐1 | Low | Low | Low | High | High |
| Suresh 2010 | Suresh (2010)‐1 | Low | High | Unclear | High | High |
| Teguh 2013 | Teguh (2013)‐1 | Low | Low | Unclear | High | High |
| Teguh (2013)‐2 | Low | Low | Unclear | High | High | |
| Teng 2021 | Teng (2021)‐1 | Low | Low | Low | High | High |
| Teng (2021)‐2 | Low | Low | Low | High | High | |
| Tenhunen 2008 | Tenhunen (2008)‐1 | High | Low | Low | High | High |
| Tenhunen (2008)‐2 | High | Low | Low | High | High | |
| Theunissen 2015 | Theunissen (2015)‐1 | High | Low | Low | High | High |
| Thor 2017 | Thor (2017)‐1 | Low | Low | Low | High | High |
| Tomasik 2021 | Tomasik (2021)‐1 | Low | Low | Low | High | High |
| Tomasik (2021)‐2 | Low | Low | Low | High | High | |
| Tsai 2017 | Tsai (2017)‐1 | High | High | High | High | High |
| Tsai (2017)‐2 | High | High | High | High | High | |
| Tsai 2023 | Tsai (2023)‐1 | Low | Low | Low | High | High |
| Tsai (2023)‐2 | Low | Low | Low | High | High | |
| Tsai (2023)‐3 | Low | Low | Low | High | High | |
| Tsai (2023)‐4 | Low | Low | Low | High | High | |
| Tsai (2023)‐5 | Low | Low | Low | High | High | |
| Tsai (2023)‐6 | Low | Low | Low | High | High | |
| Tzikas 2023 | Tzikas (2023)‐1 | Low | Unclear | Low | High | High |
| Tzikas (2023)‐2 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐3 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐4 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐5 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐6 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐7 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐8 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐9 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐10 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐11 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐12 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐13 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐14 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐15 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐16 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐17 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐18 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐19 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐20 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐21 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐22 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐23 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐24 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐25 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐26 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐27 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐28 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐29 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐30 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐31 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐32 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐33 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐34 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐35 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐36 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐37 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐38 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐39 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐40 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐41 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐42 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐43 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐44 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐45 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐46 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐47 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐48 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐49 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐50 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐51 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐52 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐53 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐54 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐55 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐56 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐57 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐58 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐59 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐60 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐61 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐62 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐63 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐64 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐65 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐66 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐67 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐68 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐69 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐70 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐71 | Low | Unclear | Low | High | High | |
| Tzikas (2023)‐72 | Low | Unclear | Low | High | High | |
| Ursino 2020 | Ursino (2020)‐1 | Low | Low | Low | High | High |
| Ursino (2020)‐2 | Low | Low | Low | High | High | |
| Ursino (2020)‐3 | Low | Low | Low | High | High | |
| Ursino (2020)‐4 | Low | Low | Low | High | High | |
| Ursino (2020)‐5 | Low | Low | Low | High | High | |
| Ursino (2020)‐6 | Low | Low | Low | High | High | |
| Van den Bosch 2021 | Van den Bosch (2021)‐1 | Low | Low | Low | Low | Low |
| Van den Bosch (2021)‐2 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐3 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐4 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐5 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐6 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐7 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐8 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐9 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐10 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐11 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐12 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐13 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐14 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐15 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐16 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐17 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐18 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐19 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐20 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐21 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐22 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐23 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐24 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐25 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐26 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐27 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐28 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐29 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐30 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐31 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐32 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐33 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐34 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐35 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐36 | Low | Low | Low | Low | Low | |
| Van Dijk 2021 | Van Dijk (2021)‐1 | Low | Unclear | Low | Unclear | Unclear |
| Van Dijk (2021)‐2 | Low | Unclear | Low | Unclear | Unclear | |
| Van Dijk (2021)‐3 | Low | Unclear | Low | Unclear | Unclear | |
| Van Dijk (2021)‐4 | Low | Unclear | Low | Unclear | Unclear | |
| Van Dijk (2021)‐5 | Low | Unclear | Low | Unclear | Unclear | |
| Van Dijk (2021)‐6 | Low | Unclear | Low | Unclear | Unclear | |
| Van Dijk (2021)‐7 | Low | Unclear | Low | Unclear | Unclear | |
| Van Rijn‐Dekker 2023 | van Rijn‐Dekker (2023)‐1 | Low | Low | Low | Low | Low |
| van Rijn‐Dekker (2023)‐2 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐3 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐4 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐5 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐6 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐7 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐8 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐9 | Low | Low | Low | Low | Low | |
| Verduijn 2023 | Verduijn (2023)‐1 | Low | Unclear | Low | High | High |
| Wang 2019 | Wang (2019)‐1 | Low | Low | High | Unclear | High |
| Wang 2020 | Wang (2020)‐1 | Low | Unclear | Low | High | High |
| Wang (2020)‐2 | Low | Unclear | Low | High | High | |
| Wang (2020)‐3 | Low | Unclear | Low | High | High | |
| Wang (2020)‐4 | Low | Unclear | Low | High | High | |
| Wang (2020)‐5 | Low | Unclear | Low | High | High | |
| Wang 2022 | Wang (2022)‐1 | Low | Unclear | Unclear | Unclear | Unclear |
| Wang 2024 | Wang (2024)‐1 | Low | Unclear | Low | High | High |
| Ward 2019 | Ward (2019)‐1 | Low | Low | Unclear | Unclear | Unclear |
| Ward (2019)‐2 | Low | Low | Unclear | Low | Unclear | |
| Wen 2021 | Wen (2021)‐1 | Low | Low | Low | Low | Low |
| Wen (2021)‐2 | Low | Low | Low | High | High | |
| Wentzel 2023 | Wentzel (2023)‐1 | High | Unclear | Low | High | High |
| Wentzel (2023)‐2 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐3 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐4 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐5 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐6 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐7 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐8 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐9 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐10 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐11 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐12 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐13 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐14 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐15 | High | Unclear | Low | High | High | |
| Wentzel (2023)‐16 | High | Unclear | Low | High | High | |
| Werbrouck 2009 | Werbrouck (2009)‐1 | Unclear | Low | Low | High | High |
| Willemsen 2020 | Willemsen (2020)‐1 | Low | Unclear | Low | High | High |
| Willemsen 2022 | Willemsen (2022)‐1 | Low | Unclear | Low | High | High |
| Wongwattananard 2023 | Wongwattananard (2023)‐1 | Low | Unclear | Low | High | High |
| Wopken 2014a | Wopken (2014a)‐1 | Low | Low | Low | Low | Low |
| Wopken 2014b | Wopken (2014b)‐1 | Low | Low | Low | High | High |
| Wu 2019 | Wu (2019)‐1 | High | Low | High | High | High |
| Wu (2019)‐2 | High | Low | High | High | High | |
| Wu (2019)‐3 | High | High | High | High | High | |
| Wu 2022 | Wu (2022)‐1 | Low | Low | Low | High | High |
| Wu (2022)‐2 | Low | Low | Low | High | High | |
| Wu (2022)‐3 | Low | Low | Low | High | High | |
| Yahya 2022 | Yahya (2022)‐1 | Low | Unclear | Low | High | High |
| Yahya (2022)‐2 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐3 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐4 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐5 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐6 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐7 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐8 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐9 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐10 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐11 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐12 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐13 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐14 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐15 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐16 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐17 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐18 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐19 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐20 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐21 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐22 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐23 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐24 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐25 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐26 | Low | Unclear | Low | High | High | |
| Yahya (2022)‐27 | Low | Unclear | Low | High | High | |
| Yan 2021 | Yan (2021)‐1 | Low | Low | Unclear | High | High |
| Yang 2021 | Yang (2021)‐1 | Low | Unclear | Unclear | High | High |
| Yang 2023a | Yang (2023a)‐1 | Low | Low | Low | High | High |
| Yang 2023b | Yang (2023b)‐1 | Low | Low | Low | High | High |
| Yu 2016 | Yu (2016)‐1 | Low | Low | High | High | High |
| Zeng 2015 | Zeng (2015)‐1 | Low | Low | High | High | High |
| Zhou 2020 | Zhou (2020)‐1 | High | Low | Unclear | High | High |
| Zhou (2020)‐2 | High | Low | Unclear | High | High | |
| Zhu 2021 | Zhu (2021)‐1 | Low | Low | Low | High | High |
| Zhu 2024 | Zhu (2024)‐1 | Low | Low | Unclear | High | High |
| Zhu (2024)‐2 | Low | Low | Unclear | High | High | |
| Zhu (2024)‐3 | Low | Low | Unclear | High | High | |
| Zhu (2024)‐4 | Low | Low | Unclear | High | High | |
| Zhu (2024)‐5 | Low | Low | Unclear | High | High | |
| Zhu (2024)‐6 | Low | Low | Unclear | High | High | |
Appendix 6. Assessment of applicability concerns of model development studies (n = 617) from 152 publications
| Publication | Study subID | PROBAST ‐ Applicability | |||
| Participants Domain | Predictors Domain | Outcome Domain | Overall Judgement | ||
| Abdollahi 2023 | Abdollahi (2023)‐1 | Low | Low | Unclear | Unclear |
| Abdollahi (2023)‐2 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐3 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐4 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐5 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐6 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐7 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐8 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐9 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐10 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐11 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐12 | Low | Low | Unclear | Unclear | |
| Abdollahi (2023)‐13 | Low | Low | Unclear | Unclear | |
| Alterio 2017 | Alterio (2017)‐1 | Low | Low | Unclear | Unclear |
| Anderson 2018 | Anderson (2018)‐1 | Low | Low | Low | Low |
| Anderson (2018)‐2 | Low | Low | Low | Low | |
| Bakhshandeh 2013 | Bakhshandeh (2012)‐1 | Low | Low | Low | Low |
| Bakhshandeh (2012)‐2 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐3 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐4 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐5 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐6 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐7 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐8 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐9 | Low | Low | Low | Low | |
| Bakhshandeh (2012)‐10 | Low | Low | Low | Low | |
| Beetz 2012a | Beetz (2012a)‐1 | Low | Low | Low | Low |
| Beetz (2012a)‐2 | Low | Low | Low | Low | |
| Beetz 2012b | Beetz (2012b)‐1 | Low | Low | Low | Low |
| Beetz (2012b)‐2 | Low | Low | Low | Low | |
| Bhide 2012 | Bhide (2012)‐1 | Unclear | Low | Low | Unclear |
| Bhide (2012)‐2 | Unclear | Low | Low | Unclear | |
| Bin 2022 | Bin (2022)‐1 | Low | Low | Low | Low |
| Blanco 2005 | Blanco (2005)‐1 | Unclear | Low | Low | Unclear |
| Blanco (2005)‐2 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐3 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐4 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐5 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐6 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐7 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐8 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐9 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐10 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐11 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐12 | Unclear | Low | Low | Unclear | |
| Blanco (2005)‐13 | Unclear | Low | Low | Unclear | |
| Boomsma 2012 | Boomsma (2012)‐1 | Low | Low | Low | Low |
| Buettner 2010 | Buettner (2010)‐1 | Low | Low | Low | Low |
| Buettner (2010)‐2 | Low | Low | Low | Low | |
| Buettner (2010)‐3 | Low | Low | Low | Low | |
| Buettner (2010)‐4 | Low | Low | Low | Low | |
| Buettner (2010)‐5 | Low | Low | Low | Low | |
| Buettner (2010)‐6 | Low | Low | Low | Low | |
| Buettner (2010)‐7 | Low | Low | Low | Low | |
| Buettner (2010)‐8 | Low | Low | Low | Low | |
| Buettner (2010)‐9 | Low | Low | Low | Low | |
| Buettner (2010)‐10 | Low | Low | Low | Low | |
| Buettner (2010)‐11 | Low | Low | Low | Low | |
| Busato 2023 | Busato (2023)‐1 | Low | Low | Low | Low |
| Busato (2023)‐2 | Low | Low | Low | Low | |
| Busato (2023)‐3 | Low | Low | Low | Low | |
| Busato (2023)‐4 | Low | Low | Low | Low | |
| Busato (2023)‐5 | Low | Low | Low | Low | |
| Busato (2023)‐6 | Low | Low | Low | Low | |
| Cavallo 2021 | Cavallo (2021)‐1 | Low | Low | Low | Low |
| Chao 2001 | Chao (2001)‐1 | Low | Low | Low | Low |
| Chao (2001)‐2 | Low | Low | Low | Low | |
| Chao (2001)‐3 | Low | Low | Low | Low | |
| Chao (2001)‐4 | Low | Low | Low | Low | |
| Chao (2001)‐5 | Low | Low | Low | Low | |
| Chao (2001)‐6 | Low | Low | Low | Low | |
| Chao (2001)‐7 | Low | Low | Low | Low | |
| Chao (2001)‐8 | Low | Low | Low | Low | |
| Chao (2001)‐9 | Low | Low | Low | Low | |
| Chao (2001)‐10 | Low | Low | Low | Low | |
| Chao 2022 | Chao (2022)‐1 | Unclear | Low | Low | Unclear |
| Chao (2022)‐2 | Unclear | Low | Low | Unclear | |
| Chao (2022)‐3 | Unclear | Low | Low | Unclear | |
| Chao (2022)‐4 | Unclear | Low | Low | Unclear | |
| Chen 2013 | Chen (2013)‐1 | Low | Low | Low | Low |
| Chen (2013)‐2 | Low | Low | Low | Low | |
| Cheng 2018 | Cheng (2018)‐1 | Unclear | Low | Low | Unclear |
| Cheng 2019 | Cheng (2019)‐1 | Unclear | Low | Low | Unclear |
| Cheraghi 2017 | Cheraghi (2017)‐1 | Low | Low | Low | Low |
| Cheraghi (2017)‐2 | Low | Low | Low | Low | |
| Cheraghi (2017)‐3 | Low | Low | Low | Low | |
| Cheraghi (2017)‐4 | Low | Low | Low | Low | |
| Cheraghi (2017)‐5 | Low | Low | Low | Low | |
| Cheraghi (2017)‐6 | Low | Low | Low | Low | |
| Chow 2019 | Chow (2019)‐1 | High | Low | High | High |
| Christianen 2012 | Christianen (2012)‐1 | Low | Low | Low | Low |
| Christianen (2012)‐2 | Low | Low | Low | Low | |
| Christianen (2012)‐3 | Low | Low | Low | Low | |
| Christianen (2012)‐4 | Low | Low | Low | Low | |
| Christianen (2012)‐5 | Low | Low | Low | Low | |
| Dale 2016 | Dale (2016)‐1 | High | Low | High | High |
| Dale (2016)‐2 | High | Low | High | High | |
| Dean 2016 | Dean (2016)‐1 | Low | Low | Low | Low |
| Dean (2016)‐2 | Low | Low | Low | Low | |
| Dean (2016)‐3 | Low | Low | Low | Low | |
| Dean (2016)‐4 | Low | Low | Low | Low | |
| Dean (2016)‐5 | Low | Low | Low | Low | |
| Dean (2016)‐6 | Low | Low | Low | Low | |
| Dean 2018 | Dean (2018)‐1 | Low | Low | Low | Low |
| Dean (2018)‐2 | Low | Low | Low | Low | |
| Dean (2018)‐3 | Low | Low | Low | Low | |
| Dean (2018)‐4 | Low | Low | Low | Low | |
| Dean (2018)‐5 | Low | Low | Low | Low | |
| Dean (2018)‐6 | Low | Low | Low | Low | |
| Deneuve 2023 | Deneuve (2023)‐1 | Unclear | Low | Low | Unclear |
| Deneuve (2023)‐2 | Unclear | Low | Low | Unclear | |
| Deneuve (2023)‐3 | Unclear | Low | Low | Unclear | |
| Deneuve (2023)‐4 | Unclear | Low | Low | Unclear | |
| Dijkema 2010 | Dijkema (2010)‐1 | Unclear | Low | Low | Unclear |
| Dijkema (2010)‐2 | Unclear | Low | Low | Unclear | |
| Dijkema (2010)‐3 | Unclear | Low | Low | Unclear | |
| Dijkema (2010)‐4 | Unclear | Low | Low | Unclear | |
| Dohopolski 2022 | Dohopolski (2022)‐1 | Unclear | Low | Low | Unclear |
| Dohopolski (2022)‐2 | Unclear | Low | Low | Unclear | |
| Dohopolski (2022)‐3 | Unclear | Low | Low | Unclear | |
| Dong 2023 | Dong (2023)‐1 | Low | Low | Unclear | Unclear |
| Dong (2023)‐2 | Low | Low | Unclear | Unclear | |
| Dong (2023)‐3 | Low | Low | Unclear | Unclear | |
| Eisbruch 1999 | Eisbruch (1999)‐1 | Unclear | Low | Low | Unclear |
| Eisbruch (1999)‐2 | Unclear | Low | Low | Unclear | |
| Eisbruch (1999)‐3 | Unclear | Low | Low | Unclear | |
| Eisbruch 2011 | Eisbruch (2011)‐1 | Unclear | Low | Low | Unclear |
| Eisbruch (2011)‐2 | Unclear | Low | Low | Unclear | |
| Eisbruch (2011)‐3 | Unclear | Low | Low | Unclear | |
| Eisbruch (2011)‐4 | Unclear | Low | Low | Unclear | |
| Eisbruch (2011)‐5 | Unclear | Low | Low | Unclear | |
| Eisbruch (2011)‐6 | Unclear | Low | Low | Unclear | |
| Eisbruch (2011)‐7 | Unclear | Low | Low | Unclear | |
| Eisbruch (2011)‐8 | Unclear | Low | Low | Unclear | |
| Fanizzi 2022 | Fanizzi (2022)‐1 | Low | Low | Unclear | Unclear |
| Fanizzi (2022)‐2 | Low | Low | Unclear | Unclear | |
| Fanizzi (2022)‐3 | Low | Low | Unclear | Unclear | |
| Fanizzi (2022)‐4 | Low | Low | Unclear | Unclear | |
| Guan 2020 | Guan (2020)‐1 | Low | Low | Low | Low |
| Gupta 2015 | Gupta (2015)‐1 | Unclear | Low | Low | Unclear |
| Gupta (2015)‐2 | Unclear | Low | Low | Unclear | |
| Gupta (2015)‐3 | Unclear | Low | Low | Unclear | |
| Gupta (2015)‐4 | Unclear | Low | Low | Unclear | |
| Hamada 2023 | Hamada (2023)‐1 | Low | Low | Low | Low |
| Hansen 2020 | Hansen (2020)‐1 | Low | Low | Low | Low |
| Hansen (2020)‐2 | Low | Low | Low | Low | |
| He 2023 | He (2023)‐1 | Low | Low | High | High |
| He (2023)‐2 | Low | Low | High | High | |
| He (2023)‐3 | Low | Low | High | High | |
| He 2024 | He (2024)‐1 | Low | Low | Low | Low |
| Hosseinian 2023 | Hosseinian (2023)‐1 | Low | Low | Low | Low |
| Houweling 2010 | Houweling (2010)‐1 | Low | Low | Low | Low |
| Houweling (2010)‐2 | Low | Low | Low | Low | |
| Houweling (2010)‐3 | Low | Low | Low | Low | |
| Houweling (2010)‐4 | Low | Low | Low | Low | |
| Houweling (2010)‐5 | Low | Low | Low | Low | |
| Houweling (2010)‐6 | Low | Low | Low | Low | |
| Huang 2022 | Huang (2022)‐1 | Low | Low | Low | Low |
| Humbert‐Vidan 2021 | Humbert‐Vidan (2021)‐1 | Low | High | Low | High |
| Humbert‐Vidan (2021)‐2 | Low | High | Low | High | |
| Humbert‐Vidan (2021)‐3 | Low | High | Low | High | |
| Humbert‐Vidan (2021)‐4 | Low | High | Low | High | |
| Humbert‐Vidan (2021)‐5 | Low | High | Low | High | |
| Humbert‐Vidan 2022 | Humbert‐Vidan (2022)‐1 | Low | Low | Low | Low |
| Humbert‐Vidan (2022)‐2 | Low | Low | Low | Low | |
| Kamstra 2015 | Kamstra (2015)‐1 | Low | Low | Low | Low |
| Kamstra (2015)‐2 | Low | Low | Low | Low | |
| Kanehira 2021 | Kanehira (2021)‐1 | Low | Low | Low | Low |
| Kanehira (2021)‐2 | Low | Low | Low | Low | |
| Kanehira (2021)‐3 | Low | Low | Low | Low | |
| Kanehira (2021)‐4 | Low | Low | Low | Low | |
| Kanehira (2021)‐5 | Low | Low | Low | Low | |
| Kanehira (2021)‐6 | Low | Low | Low | Low | |
| Kanehira (2021)‐7 | Low | Low | Low | Low | |
| Karsten 2019 | Karsten (2019)‐1 | High | Low | Low | High |
| Kawamura 2019 | Kawamura (2019)‐1 | High | Low | Low | High |
| Kraaijenga 2019 | Kraaijenga (2019)‐1 | Low | Low | Low | Low |
| Kraaijenga (2019)‐2 | Low | Low | Low | Low | |
| Krasin 2012 | Krasin (2012)‐1 | Unclear | Low | Low | Unclear |
| Krasin (2012)‐2 | Unclear | Low | Low | Unclear | |
| Krasin (2012)‐3 | Unclear | Low | Low | Unclear | |
| Langendijk 2009 | Langendijk (2009)‐1 | Low | Low | Low | Low |
| Langius 2016 | Langius (2016)‐1 | Low | Low | Low | Low |
| Langius (2016)‐2 | Low | Low | Low | Low | |
| Lee 2012 | Lee (2012)‐1 | Low | Low | Low | Low |
| Lee (2012)‐2 | Low | Low | Low | Low | |
| Lee 2014a | Lee (2014a)‐1 | Unclear | Low | Low | Unclear |
| Lee (2014a)‐2 | Unclear | Low | Low | Unclear | |
| Lee 2014b | Lee (2014b)‐1 | Low | Low | Low | Low |
| Lee (2014b)‐2 | Low | Low | Low | Low | |
| Lee (2014b)‐3 | Low | Low | Low | Low | |
| Lee (2014b)‐4 | Low | Low | Low | Low | |
| Lee (2014b)‐5 | Low | Low | Low | Low | |
| Lee (2014b)‐6 | Low | Low | Low | Low | |
| Lee (2014b)‐7 | Low | Low | Low | Low | |
| Lee (2014b)‐8 | Low | Low | Low | Low | |
| Lee (2014b)‐9 | Low | Low | Low | Low | |
| Lee (2014b)‐10 | Low | Low | Low | Low | |
| Lee (2014b)‐11 | Low | Low | Low | Low | |
| Lee (2014b)‐12 | Low | Low | Low | Low | |
| Lee 2015a | Lee (2015a)‐1 | High | Low | Low | High |
| Lee (2015a)‐2 | High | Low | Low | High | |
| Lee 2015b | Lee (2015b)‐1 | Low | Low | Low | Low |
| Lee (2015b)‐2 | Low | Low | Low | Low | |
| Lee (2015b)‐3 | Low | Low | Low | Low | |
| Lee (2015b)‐4 | Low | Low | Low | Low | |
| Lee (2015b)‐5 | Low | Low | Low | Low | |
| Lee (2015b)‐6 | Low | Low | Low | Low | |
| Lee 2023 | Lee (2023)‐1 | Low | Low | Low | Low |
| Lee (2023)‐2 | Low | Low | Low | Low | |
| Lee (2023)‐3 | Low | Low | Low | Low | |
| Lescut 2013 | Lescut (2013)‐1 | Low | High | High | High |
| Lescut (2013)‐2 | Low | High | High | High | |
| Li 2020 | Li (2020)‐1 | Low | Low | Low | Low |
| Li (2020)‐2 | Low | Low | Low | Low | |
| Li 2022a | Li (2022a)‐1 | Low | Low | Low | Low |
| Li (2022a)‐2 | Low | Low | Low | Low | |
| Li 2022b | Li (2022b)‐1 | Low | Low | Unclear | Unclear |
| Li (2022b)‐2 | Low | Low | Unclear | Unclear | |
| Li (2022b)‐3 | Low | Low | Unclear | Unclear | |
| Lindblom 2014 | Lindblom (2014)‐1 | Low | Low | High | High |
| Lindblom (2014)‐2 | Low | Low | High | High | |
| Ling 2020 | Ling (2020)‐1 | High | Low | Low | High |
| Ling (2020)‐2 | High | Low | Low | High | |
| Ling (2020)‐3 | High | Low | Low | High | |
| Ling (2020)‐4 | High | Low | Low | High | |
| Liu 2019 | Liu (2019)‐1 | Low | Low | Low | Low |
| Liu (2019)‐2 | Low | Low | Low | Low | |
| Liu 2022 | Liu (2022)‐1 | Low | Low | Low | Low |
| Luo 2017 | Luo (2017)‐1 | Low | Low | Low | Low |
| Luo 2018 | Luo (2018)‐1 | Low | Low | Low | Low |
| Marzi 2009 | Marzi (2009)‐1 | Low | Low | Low | Low |
| Marzi (2009)‐2 | Low | Low | Low | Low | |
| Marzi (2009)‐3 | Low | Low | Low | Low | |
| Marzi (2009)‐4 | Low | Low | Low | Low | |
| Marzi (2009)‐5 | Low | Low | Low | Low | |
| Marzi (2009)‐6 | Low | Low | Low | Low | |
| Marzi 2021 | Marzi (2021)‐1 | Low | Low | Low | Low |
| Mavroidis 2003 | Mavroidis (2003)‐1 | Low | Low | Unclear | Unclear |
| Mavroidis 2017 | Mavroidis (2017)‐1 | Low | Low | Low | Low |
| Mavroidis (2017)‐2 | Low | Low | Low | Low | |
| Mavroidis (2017)‐3 | Low | Low | Low | Low | |
| Mavroidis (2017)‐4 | Low | Low | Low | Low | |
| Mavroidis (2017)‐5 | Low | Low | Low | Low | |
| Mavroidis (2017)‐6 | Low | Low | Low | Low | |
| Mavroidis (2017)‐7 | Low | Low | Low | Low | |
| Mavroidis (2017)‐8 | Low | Low | Low | Low | |
| Mavroidis (2017)‐9 | Low | Low | Low | Low | |
| Mavroidis (2017)‐10 | Low | Low | Low | Low | |
| Mavroidis (2017)‐11 | Low | Low | Low | Low | |
| Mavroidis (2017)‐12 | Low | Low | Low | Low | |
| Mavroidis (2017)‐13 | Low | Low | Low | Low | |
| Mavroidis (2017)‐14 | Low | Low | Low | Low | |
| Mavroidis (2017)‐15 | Low | Low | Low | Low | |
| Mavroidis (2017)‐16 | Low | Low | Low | Low | |
| Mavroidis (2017)‐17 | Low | Low | Low | Low | |
| Mavroidis (2017)‐18 | Low | Low | Low | Low | |
| Mavroidis (2017)‐19 | Low | Low | Low | Low | |
| Mavroidis (2017)‐20 | Low | Low | Low | Low | |
| Mavroidis 2018 | Mavroidis (2018)‐1 | Low | Low | Unclear | Unclear |
| Mavroidis (2018)‐2 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐3 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐4 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐5 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐6 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐7 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐8 | Low | Low | Unclear | Unclear | |
| Mavroidis (2018)‐9 | Low | Low | Unclear | Unclear | |
| Men 2019 | Men (2019)‐1 | Low | Low | Low | Low |
| Mori 2019 | Mori (2019)‐1 | Low | Low | Low | Low |
| Mori (2019)‐2 | Low | Low | Low | Low | |
| Morimoto 2019 | Morimoto (2019)‐1 | Low | Low | Low | Low |
| Murthy 2018 | Murthy (2018)‐1 | Low | Low | Low | Low |
| Murthy (2018)‐2 | Low | Low | Low | Low | |
| Musha 2020 | Musha (2020)‐1 | Unclear | Low | Low | Unclear |
| Musha (2020)‐2 | Unclear | Low | Low | Unclear | |
| Musha (2020)‐3 | Unclear | Low | Low | Unclear | |
| Musha (2020)‐4 | Unclear | Low | Low | Unclear | |
| Nourissat 2010 | Nourissat (2010)‐1 | Low | Low | Low | Low |
| Nourissat (2010)‐2 | Low | Low | Low | Low | |
| Onjukka 2020 | Onjukka (2020)‐1 | Low | Low | Low | Low |
| Onjukka (2020)‐2 | Low | Low | Low | Low | |
| Onjukka (2020)‐3 | Low | Low | Low | Low | |
| Onjukka (2020)‐4 | Low | Low | Low | Low | |
| Orlandi 2018 | Orlandi (2018)‐1 | Low | Low | Low | Low |
| Orlandi (2018)‐2 | Low | Low | Low | Low | |
| OuYang 2023 | OuYang (2023)‐1 | Low | Low | Low | Low |
| Padannayil 2023 | Padannayil (2023)‐1 | Unclear | Low | Unclear | Unclear |
| Padannayil (2023)‐2 | Unclear | Low | Unclear | Unclear | |
| Pan 2020a | Pan (2020a)‐1 | Low | Low | Low | Low |
| Pan 2020b | Pan (2020b)‐1 | Low | Low | Low | Low |
| Pan (2020b)‐2 | Low | Low | Low | Low | |
| Peng 2020 | Peng (2020)‐1 | Low | Low | Low | Low |
| Peng (2020)‐2 | Low | Low | Low | Low | |
| Peng (2020)‐3 | Low | Low | Low | Low | |
| Peuker 2022 | Peuker (2022)‐1 | Low | Low | Unclear | Unclear |
| Peuker (2022)‐2 | Low | Low | Unclear | Unclear | |
| Pota 2017 | Pota (2017)‐1 | Low | Low | Low | Low |
| Pota (2017)‐2 | Low | Low | Low | Low | |
| Pota (2017)‐3 | Low | Low | Low | Low | |
| Qin 2023 | Qin (2023)‐1 | Low | Low | Low | Low |
| Rachi 2023 | Rachi (2023)‐1 | Unclear | Low | High | High |
| Rachi (2023)‐2 | Unclear | Low | High | High | |
| Rades 2022 | Rades (2022)‐1 | Unclear | Unclear | High | High |
| Rancati 2009 | Rancati (2009)‐1 | Low | Low | Low | Low |
| Rancati (2009)‐2 | Low | Low | Low | Low | |
| Rao 2016 | Rao (2016)‐1 | Low | Low | Low | Low |
| Rao (2016)‐2 | Low | Low | Low | Low | |
| Rao (2016)‐3 | Low | Low | Low | Low | |
| Ren 2021 | Ren (2021)‐1 | Unclear | Low | Low | Unclear |
| Ren (2021)‐2 | Unclear | Low | Low | Unclear | |
| Renda 2020 | Renda (2019)‐1 | Low | Low | Unclear | Unclear |
| Renda (2019)‐2 | Low | Low | Unclear | Unclear | |
| Ritlumlert 2023 | Ritlumlert (2023)‐1 | Low | Low | Unclear | Unclear |
| Ritlumlert (2023)‐2 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐3 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐4 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐5 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐6 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐7 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐8 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐9 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐10 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐11 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐12 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐13 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐14 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐15 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐16 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐17 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐18 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐19 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐20 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐21 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐22 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐23 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐24 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐25 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐26 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐27 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐28 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐29 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐30 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐31 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐32 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐33 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐34 | Low | Low | Unclear | Unclear | |
| Ritlumlert (2023)‐35 | Low | Low | Unclear | Unclear | |
| Ronjom 2013 | Ronjom (2013)‐1 | Low | Low | Low | Low |
| Ronjom (2013)‐2 | Low | Low | Low | Low | |
| Ronjom 2015 | Ronjom (2015)‐1 | Low | Low | Low | Low |
| Rosen 2018 | Rosen (2018)‐1 | Low | Low | Low | Low |
| Rosen (2018)‐2 | Low | Low | Low | Low | |
| Rosen (2018)‐3 | Low | Low | Low | Low | |
| Rosen (2018)‐4 | Low | Low | Low | Low | |
| Rwigema 2019 | Rwigema (2019)‐1 | Low | Low | Low | Low |
| Rwigema (2019)‐2 | Low | Low | Low | Low | |
| Rwigema (2019)‐3 | Low | Low | Low | Low | |
| Rwigema (2019)‐4 | Low | Low | Low | Low | |
| Rwigema (2019)‐5 | Low | Low | Low | Low | |
| Schuette 2020 | Schuette (2020)‐1 | Low | Low | Low | Low |
| Schuette (2020)‐2 | Low | Low | Low | Low | |
| Scrimger 2004 | Scrimger (2004)‐1 | Low | Low | Low | Low |
| Sheikh 2019 | Sheikh (2019)‐1 | Low | Low | Low | Low |
| Sheikh (2019)‐2 | Low | Low | Low | Low | |
| Sheikh (2019)‐3 | Low | Low | Low | Low | |
| Sheikh (2019)‐4 | Low | Low | Low | Low | |
| Sheikh (2019)‐5 | Low | Low | Low | Low | |
| Sheikh (2019)‐6 | Low | Low | Low | Low | |
| Sheikh (2019)‐7 | Low | Low | Low | Low | |
| Sheikh (2019)‐8 | Low | Low | Low | Low | |
| Shen 2021 | Shen (2021)‐1 | Low | Low | Low | Low |
| Sheu 2014 | Sheu (2014)‐1 | Low | Low | Low | Low |
| Shirai 2017 | Shirai (2017)‐1 | Low | Low | Low | Low |
| Shorter 2017 | Shorter (2017)‐1 | Low | Low | Unclear | Unclear |
| Sijtsema 2023 | Sijtsema (2023)‐1 | High | Low | Low | High |
| Singh 2023 | Singh (2023)‐1 | Unclear | Unclear | Low | Unclear |
| Singh (2023)‐2 | Unclear | Unclear | Low | Unclear | |
| Singh (2023)‐3 | Unclear | Unclear | Low | Unclear | |
| Singh (2023)‐4 | Unclear | Unclear | Low | Unclear | |
| Singla 2021 | Singla (2021)‐1 | Low | Low | Low | Low |
| Soares 2014 | Soares (2014)‐1 | Low | Low | Unclear | Unclear |
| Soares 2018 | Soares (2018)‐1 | Low | Low | Low | Low |
| Soderstrom 2017 | Soderstrom (2017)‐1 | High | Low | Low | High |
| Somay 2023 | Somay (2023)‐1 | Low | Low | Low | Low |
| Suresh 2010 | Suresh (2010)‐1 | Low | High | Unclear | High |
| Teguh 2013 | Teguh (2013)‐1 | Low | Low | Unclear | Unclear |
| Teguh (2013)‐2 | Low | Low | Unclear | Unclear | |
| Teng 2021 | Teng (2021)‐1 | Low | Low | Low | Low |
| Teng (2021)‐2 | Low | Low | Low | Low | |
| Tenhunen 2008 | Tenhunen (2008)‐1 | High | Low | Low | High |
| Tenhunen (2008)‐2 | High | Low | Low | High | |
| Theunissen 2015 | Theunissen (2015)‐1 | High | Low | Low | High |
| Thor 2017 | Thor (2017)‐1 | Low | Low | Low | Low |
| Tomasik 2021 | Tomasik (2021)‐1 | Low | Low | Low | Low |
| Tomasik (2021)‐2 | Low | Low | Low | Low | |
| Tsai 2017 | Tsai (2017)‐1 | Low | Unclear | High | High |
| Tsai (2017)‐2 | Low | Unclear | High | High | |
| Tsai 2023 | Tsai (2023)‐1 | Low | Low | Low | Low |
| Tsai (2023)‐2 | Low | Low | Low | Low | |
| Tsai (2023)‐3 | Low | Low | Low | Low | |
| Tsai (2023)‐4 | Low | Low | Low | Low | |
| Tsai (2023)‐5 | Low | Low | Low | Low | |
| Tsai (2023)‐6 | Low | Low | Low | Low | |
| Tzikas 2023 | Tzikas (2023)‐1 | Low | Low | Low | Low |
| Tzikas (2023)‐2 | Low | Low | Low | Low | |
| Tzikas (2023)‐3 | Low | Low | Low | Low | |
| Tzikas (2023)‐4 | Low | Low | Low | Low | |
| Tzikas (2023)‐5 | Low | Low | Low | Low | |
| Tzikas (2023)‐6 | Low | Low | Low | Low | |
| Tzikas (2023)‐7 | Low | Low | Low | Low | |
| Tzikas (2023)‐8 | Low | Low | Low | Low | |
| Tzikas (2023)‐9 | Low | Low | Low | Low | |
| Tzikas (2023)‐10 | Low | Low | Low | Low | |
| Tzikas (2023)‐11 | Low | Low | Low | Low | |
| Tzikas (2023)‐12 | Low | Low | Low | Low | |
| Tzikas (2023)‐13 | Low | Low | Low | Low | |
| Tzikas (2023)‐14 | Low | Low | Low | Low | |
| Tzikas (2023)‐15 | Low | Low | Low | Low | |
| Tzikas (2023)‐16 | Low | Low | Low | Low | |
| Tzikas (2023)‐17 | Low | Low | Low | Low | |
| Tzikas (2023)‐18 | Low | Low | Low | Low | |
| Tzikas (2023)‐19 | Low | Low | Low | Low | |
| Tzikas (2023)‐20 | Low | Low | Low | Low | |
| Tzikas (2023)‐21 | Low | Low | Low | Low | |
| Tzikas (2023)‐22 | Low | Low | Low | Low | |
| Tzikas (2023)‐23 | Low | Low | Low | Low | |
| Tzikas (2023)‐24 | Low | Low | Low | Low | |
| Tzikas (2023)‐25 | Low | Low | Low | Low | |
| Tzikas (2023)‐26 | Low | Low | Low | Low | |
| Tzikas (2023)‐27 | Low | Low | Low | Low | |
| Tzikas (2023)‐28 | Low | Low | Low | Low | |
| Tzikas (2023)‐29 | Low | Low | Low | Low | |
| Tzikas (2023)‐30 | Low | Low | Low | Low | |
| Tzikas (2023)‐31 | Low | Low | Low | Low | |
| Tzikas (2023)‐32 | Low | Low | Low | Low | |
| Tzikas (2023)‐33 | Low | Low | Low | Low | |
| Tzikas (2023)‐34 | Low | Low | Low | Low | |
| Tzikas (2023)‐35 | Low | Low | Low | Low | |
| Tzikas (2023)‐36 | Low | Low | Low | Low | |
| Tzikas (2023)‐37 | Low | Low | Low | Low | |
| Tzikas (2023)‐38 | Low | Low | Low | Low | |
| Tzikas (2023)‐39 | Low | Low | Low | Low | |
| Tzikas (2023)‐40 | Low | Low | Low | Low | |
| Tzikas (2023)‐41 | Low | Low | Low | Low | |
| Tzikas (2023)‐42 | Low | Low | Low | Low | |
| Tzikas (2023)‐43 | Low | Low | Low | Low | |
| Tzikas (2023)‐44 | Low | Low | Low | Low | |
| Tzikas (2023)‐45 | Low | Low | Low | Low | |
| Tzikas (2023)‐46 | Low | Low | Low | Low | |
| Tzikas (2023)‐47 | Low | Low | Low | Low | |
| Tzikas (2023)‐48 | Low | Low | Low | Low | |
| Tzikas (2023)‐49 | Low | Low | Low | Low | |
| Tzikas (2023)‐50 | Low | Low | Low | Low | |
| Tzikas (2023)‐51 | Low | Low | Low | Low | |
| Tzikas (2023)‐52 | Low | Low | Low | Low | |
| Tzikas (2023)‐53 | Low | Low | Low | Low | |
| Tzikas (2023)‐54 | Low | Low | Low | Low | |
| Tzikas (2023)‐55 | Low | Low | Low | Low | |
| Tzikas (2023)‐56 | Low | Low | Low | Low | |
| Tzikas (2023)‐57 | Low | Low | Low | Low | |
| Tzikas (2023)‐58 | Low | Low | Low | Low | |
| Tzikas (2023)‐59 | Low | Low | Low | Low | |
| Tzikas (2023)‐60 | Low | Low | Low | Low | |
| Tzikas (2023)‐61 | Low | Low | Low | Low | |
| Tzikas (2023)‐62 | Low | Low | Low | Low | |
| Tzikas (2023)‐63 | Low | Low | Low | Low | |
| Tzikas (2023)‐64 | Low | Low | Low | Low | |
| Tzikas (2023)‐65 | Low | Low | Low | Low | |
| Tzikas (2023)‐66 | Low | Low | Low | Low | |
| Tzikas (2023)‐67 | Low | Low | Low | Low | |
| Tzikas (2023)‐68 | Low | Low | Low | Low | |
| Tzikas (2023)‐69 | Low | Low | Low | Low | |
| Tzikas (2023)‐70 | Low | Low | Low | Low | |
| Tzikas (2023)‐71 | Low | Low | Low | Low | |
| Tzikas (2023)‐72 | Low | Low | Low | Low | |
| Ursino 2020 | Ursino (2020)‐1 | Low | Low | Low | Low |
| Ursino (2020)‐2 | Low | Low | Low | Low | |
| Ursino (2020)‐3 | Low | Low | Low | Low | |
| Ursino (2020)‐4 | Low | Low | Low | Low | |
| Ursino (2020)‐5 | Low | Low | Low | Low | |
| Ursino (2020)‐6 | Low | Low | Low | Low | |
| Van den Bosch 2021 | Van den Bosch (2021)‐1 | Low | Low | Low | Low |
| Van den Bosch (2021)‐2 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐3 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐4 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐5 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐6 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐7 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐8 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐9 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐10 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐11 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐12 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐13 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐14 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐15 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐16 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐17 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐18 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐19 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐20 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐21 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐22 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐23 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐24 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐25 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐26 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐27 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐28 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐29 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐30 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐31 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐32 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐33 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐34 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐35 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐36 | Low | Low | Low | Low | |
| Van Dijk 2021 | Van Dijk (2021)‐1 | Low | Low | Low | Low |
| Van Dijk (2021)‐2 | Low | Low | Low | Low | |
| Van Dijk (2021)‐3 | Low | Low | Low | Low | |
| Van Dijk (2021)‐4 | Low | Low | Low | Low | |
| Van Dijk (2021)‐5 | Low | Low | Low | Low | |
| Van Dijk (2021)‐6 | Low | Low | Low | Low | |
| Van Dijk (2021)‐7 | Low | Low | Low | Low | |
| Van Rijn‐Dekker 2023 | van Rijn‐Dekker (2023)‐1 | Low | Low | Low | Low |
| van Rijn‐Dekker (2023)‐2 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐3 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐4 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐5 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐6 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐7 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐8 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐9 | Low | Low | Low | Low | |
| Verduijn 2023 | Verduijn (2023)‐1 | Low | Low | Low | Low |
| Wang 2019 | Wang (2019)‐1 | Low | Low | High | High |
| Wang 2020 | Wang (2020)‐1 | Low | Low | Low | Low |
| Wang (2020)‐2 | Low | Low | Low | Low | |
| Wang (2020)‐3 | Low | Low | Low | Low | |
| Wang (2020)‐4 | Low | Low | Low | Low | |
| Wang (2020)‐5 | Low | Low | Low | Low | |
| Wang 2022 | Wang (2022)‐1 | Low | Low | Low | Low |
| Wang 2024 | Wang (2024)‐1 | Low | Low | Low | Low |
| Ward 2019 | Ward (2019)‐1 | Low | Low | Unclear | Unclear |
| Ward (2019)‐2 | Low | Low | Low | Low | |
| Wen 2021 | Wen (2021)‐1 | Low | Low | Low | Low |
| Wen (2021)‐2 | Low | Low | Low | Low | |
| Wentzel 2023 | Wentzel (2023)‐1 | Low | Low | Low | Low |
| Wentzel (2023)‐2 | Low | Low | Low | Low | |
| Wentzel (2023)‐3 | Low | Low | Low | Low | |
| Wentzel (2023)‐4 | Low | Low | Low | Low | |
| Wentzel (2023)‐5 | Low | Low | Low | Low | |
| Wentzel (2023)‐6 | Low | Low | Low | Low | |
| Wentzel (2023)‐7 | Low | Low | Low | Low | |
| Wentzel (2023)‐8 | Low | Low | Low | Low | |
| Wentzel (2023)‐9 | Low | Low | Low | Low | |
| Wentzel (2023)‐10 | Low | Low | Low | Low | |
| Wentzel (2023)‐11 | Low | Low | Low | Low | |
| Wentzel (2023)‐12 | Low | Low | Low | Low | |
| Wentzel (2023)‐13 | Low | Low | Low | Low | |
| Wentzel (2023)‐14 | Low | Low | Low | Low | |
| Wentzel (2023)‐15 | Low | Low | Low | Low | |
| Wentzel (2023)‐16 | Low | Low | Low | Low | |
| Werbrouck 2009 | Werbrouck (2009)‐1 | Unclear | High | Low | High |
| Willemsen 2020 | Willemsen (2020)‐1 | Low | Low | Low | Low |
| Willemsen 2022 | Willemsen (2022)‐1 | Low | Low | Low | Low |
| Wongwattananard 2023 | Wongwattananard (2023)‐1 | Low | Low | Low | Low |
| Wopken 2014a | Wopken (2014a)‐1 | Low | Low | Low | Low |
| Wopken 2014b | Wopken (2014b)‐1 | Low | Low | Low | Low |
| Wu 2019 | Wu (2019)‐1 | Low | Unclear | High | High |
| Wu (2019)‐2 | Low | Low | High | High | |
| Wu (2019)‐3 | Low | Unclear | High | High | |
| Wu 2022 | Wu (2022)‐1 | Low | Low | Low | Low |
| Wu (2022)‐2 | Low | Low | Low | Low | |
| Wu (2022)‐3 | Low | Low | Low | Low | |
| Yahya 2022 | Yahya (2022)‐1 | Low | Low | Low | Low |
| Yahya (2022)‐2 | Low | Low | Low | Low | |
| Yahya (2022)‐3 | Low | Low | Low | Low | |
| Yahya (2022)‐4 | Low | Low | Low | Low | |
| Yahya (2022)‐5 | Low | Low | Low | Low | |
| Yahya (2022)‐6 | Low | Low | Low | Low | |
| Yahya (2022)‐7 | Low | Low | Low | Low | |
| Yahya (2022)‐8 | Low | Low | Low | Low | |
| Yahya (2022)‐9 | Low | Low | Low | Low | |
| Yahya (2022)‐10 | Low | Low | Low | Low | |
| Yahya (2022)‐11 | Low | Low | Low | Low | |
| Yahya (2022)‐12 | Low | Low | Low | Low | |
| Yahya (2022)‐13 | Low | Low | Low | Low | |
| Yahya (2022)‐14 | Low | Low | Low | Low | |
| Yahya (2022)‐15 | Low | Low | Low | Low | |
| Yahya (2022)‐16 | Low | Low | Low | Low | |
| Yahya (2022)‐17 | Low | Low | Low | Low | |
| Yahya (2022)‐18 | Low | Low | Low | Low | |
| Yahya (2022)‐19 | Low | Low | Low | Low | |
| Yahya (2022)‐20 | Low | Low | Low | Low | |
| Yahya (2022)‐21 | Low | Low | Low | Low | |
| Yahya (2022)‐22 | Low | Low | Low | Low | |
| Yahya (2022)‐23 | Low | Low | Low | Low | |
| Yahya (2022)‐24 | Low | Low | Low | Low | |
| Yahya (2022)‐25 | Low | Low | Low | Low | |
| Yahya (2022)‐26 | Low | Low | Low | Low | |
| Yahya (2022)‐27 | Low | Low | Low | Low | |
| Yan 2021 | Yan (2021)‐1 | Low | Low | Low | Low |
| Yang 2021 | Yang (2021)‐1 | Low | Low | Low | Low |
| Yang 2023a | Yang (2023a)‐1 | Unclear | Low | Low | Unclear |
| Yang 2023b | Yang (2023b)‐1 | Low | Low | Low | Low |
| Yu 2016 | Yu (2016)‐1 | Low | Unclear | High | High |
| Zeng 2015 | Zeng (2015)‐1 | Low | Low | High | High |
| Zhou 2020 | Zhou (2020)‐1 | Low | Low | Unclear | Unclear |
| Zhou (2020)‐2 | Low | Low | Unclear | Unclear | |
| Zhu 2021 | Zhu (2021)‐1 | Low | Low | Low | Low |
| Zhu 2024 | Zhu (2024)‐1 | Unclear | Low | Unclear | Unclear |
| Zhu (2024)‐2 | Unclear | Low | Unclear | Unclear | |
| Zhu (2024)‐3 | Unclear | Low | Unclear | Unclear | |
| Zhu (2024)‐4 | Unclear | Low | Unclear | Unclear | |
| Zhu (2024)‐5 | Unclear | Low | Unclear | Unclear | |
| Zhu (2024)‐6 | Unclear | Low | Unclear | Unclear | |
Appendix 7. Risk of bias assessment of external validation studies (n = 193) from 39 publications
| Publication | Study subID | PROBAST ‐ Risk of Bias | ||||
| Participants Domain | Predictors Domain | Outcome Domain | Analysis Domain | Overall Judgement | ||
| Beetz 2012c | Beetz (2012c)‐1 | Low | Low | Low | High | High |
| Beetz (2012c)‐2 | Low | Low | Low | High | High | |
| Blanchard 2016 | Blanchard (2016)‐1 | Low | Low | Low | High | High |
| Blanchard (2016)‐2 | Low | Low | Low | High | High | |
| Blanchard (2016)‐3 | Low | Low | Low | High | High | |
| Blanchard (2016)‐4 | Low | Low | Low | High | High | |
| Blanchard (2016)‐5 | Low | Low | Low | High | High | |
| Buettner 2012 | Buettner (2012)‐1 | Unclear | Low | Low | High | High |
| Buettner (2012)‐2 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐3 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐4 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐5 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐6 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐7 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐8 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐9 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐10 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐11 | Low | Low | Low | High | High | |
| Buettner (2012)‐12 | Low | Low | Low | High | High | |
| Buettner (2012)‐13 | Low | Low | Low | High | High | |
| Buettner (2012)‐14 | Low | Low | Low | High | High | |
| Buettner (2012)‐15 | Low | Low | Low | High | High | |
| Buettner (2012)‐16 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐17 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐18 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐19 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐20 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐21 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐22 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐23 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐24 | Unclear | Low | Low | High | High | |
| Buettner (2012)‐25 | Unclear | Low | Low | High | High | |
| Cavallo 2021 | Cavallo (2021)‐1 | Unclear | Low | Low | High | High |
| Cavallo (2021)‐2 | Unclear | Low | Low | High | High | |
| Chao 2022 | Chao (2022)‐1 | Unclear | Low | Low | High | High |
| Chao (2022)‐2 | Unclear | Low | Low | High | High | |
| Chao (2022)‐3 | Unclear | Low | Low | High | High | |
| Chao (2022)‐4 | Unclear | Low | Low | High | High | |
| Christianen 2016 | Christianen (2016)‐1 | Low | Low | Low | High | High |
| Dean 2018 | Dean (2018)‐1 | Unclear | Low | Unclear | High | High |
| Dean (2018)‐2 | Unclear | Low | Unclear | High | High | |
| Dean (2018)‐3 | Unclear | Low | Unclear | High | High | |
| Dean (2018)‐4 | Unclear | Low | Unclear | High | High | |
| Dean (2018)‐5 | Unclear | Low | Unclear | High | High | |
| Dean (2018)‐6 | Unclear | Low | Unclear | High | High | |
| Deneuve 2023 | Deneuve (2023)‐1 | Unclear | Low | Low | High | High |
| Deneuve (2023)‐2 | Unclear | Low | Low | High | High | |
| Deneuve (2023)‐3 | Unclear | Low | Low | High | High | |
| Deneuve (2023)‐4 | Unclear | Low | Low | High | High | |
| Deutsch 2021 | Deutsch (2020)‐1 | Low | Low | Low | High | High |
| Hansen 2019 | Hansen (2019)‐1 | Unclear | Low | Low | High | High |
| Huynh 2023 | Huynh (2023)‐1 | High | Low | High | High | High |
| Huynh (2023)‐2 | High | Low | High | High | High | |
| Huynh (2023)‐3 | High | Low | High | High | High | |
| Huynh (2023)‐4 | High | Low | High | High | High | |
| Huynh (2023)‐5 | High | Low | High | High | High | |
| Huynh (2023)‐6 | High | Low | High | High | High | |
| Kalendralis 2022 | Kalendralis (2022)‐1 | Low | Low | Low | High | High |
| Kamal 2020 | Kamal (2020)‐1 | High | Low | High | Low | High |
| Kamal (2020)‐2 | High | Low | High | Low | High | |
| Kanayama 2018 | Kanayama (2018)‐1 | Unclear | Low | Low | High | High |
| Kinclova 2020 | Kinclova (2020)‐1 | High | Low | Low | High | High |
| Kinclova (2020)‐2 | High | Low | Low | High | High | |
| Kinclova (2020)‐3 | High | Low | Low | High | High | |
| Koiwai 2010 | Koiwai (2010)‐1 | High | Low | High | High | High |
| Koiwai (2010)‐2 | High | Low | High | High | High | |
| Langendijk 2021 | Langendijk‐2021‐1 | Low | Low | Low | Low | Low |
| Langendijk‐2021‐2 | Low | Low | Low | Low | Low | |
| Langendijk‐2021‐3 | Low | Low | Low | Low | Low | |
| Langendijk‐2021‐4 | Low | Low | Low | Low | Low | |
| Langendijk‐2021‐5 | Low | Low | Low | High | High | |
| Langendijk‐2021‐6 | Low | Low | Low | High | High | |
| Lee 2014b | Lee (2014b)‐1 | Unclear | Low | Low | High | High |
| Lee (2014b)‐2 | Unclear | Low | Low | High | High | |
| Lee (2014b)‐3 | Unclear | Low | Low | High | High | |
| Lee (2014b)‐4 | Unclear | Low | Low | High | High | |
| Li 2023 | Li (2023)‐1 | Low | Low | Low | High | High |
| Li (2023)‐2 | Low | Low | Low | High | High | |
| Li (2023)‐3 | Low | Low | Low | High | High | |
| Luo 2018 | Luo (2018)‐1 | Low | Low | Low | High | High |
| Luo (2018)‐2 | Low | Low | Low | High | High | |
| Luo (2018)‐3 | Low | Low | Low | High | High | |
| Luo (2018)‐4 | Low | Low | Low | High | High | |
| Nevens 2016 | Nevens (2016)‐1 | Low | Low | Low | Low | Low |
| Nevens (2016)‐2 | Low | Low | Low | Low | Low | |
| Nevens (2016)‐3 | Low | Low | Low | Low | Low | |
| Nevens (2016)‐4 | Low | Low | Low | Low | Low | |
| Nevens (2016)‐5 | Low | Low | Low | Low | Low | |
| Nowicka 2020 | Nowicka (2020)‐1 | Low | Low | Unclear | High | High |
| Nowicka (2020)‐2 | Low | Low | Unclear | High | High | |
| Nowicka (2020)‐3 | Low | Low | Unclear | High | High | |
| Nowicka (2020)‐4 | Low | Low | Unclear | High | High | |
| Nowicka (2020)‐5 | Low | Low | Unclear | High | High | |
| OuYang 2023 | OuYang (2023)‐1 | Low | Low | Low | Low | Low |
| OuYang (2023)‐2 | Low | Low | Low | Low | Low | |
| OuYang (2023)‐3 | Low | Low | Low | Low | Low | |
| OuYang (2023)‐4 | Low | Low | Low | Low | Low | |
| Ronjom 2015 | Ronjom (2015)‐1 | Low | Low | Low | High | High |
| Sharabiani 2021 | Sharabiani (2021)‐1 | Low | Low | Low | High | High |
| Sharabiani (2021)‐2 | Low | Low | Low | High | High | |
| Sharabiani (2021)‐3 | Low | Low | Low | High | High | |
| Sharabiani (2021)‐4 | Low | Low | Low | High | High | |
| Sharabiani (2021)‐5 | Low | Low | Low | High | High | |
| Thor 2017 | Thor (2017)‐1 | Unclear | Low | Low | High | High |
| Tsai 2023 | Tsai (2023)‐1 | Low | Low | Low | High | High |
| Tsai (2023)‐2 | Low | Low | Low | High | High | |
| Tsai (2023)‐3 | Low | Low | Low | High | High | |
| Tsai (2023)‐4 | Low | Low | Low | High | High | |
| Tsai (2023)‐5 | Low | Low | Low | High | High | |
| Tsai (2023)‐6 | Low | Low | Low | High | High | |
| Tsai (2023)‐7 | Low | Low | Low | High | High | |
| Tsai (2023)‐8 | Low | Low | Low | High | High | |
| Tsai (2023)‐9 | Low | Low | Low | High | High | |
| Tsai (2023)‐10 | Low | Low | Low | High | High | |
| Tsai (2023)‐11 | Low | Low | Low | High | High | |
| Tsai (2023)‐12 | Low | Low | Low | High | High | |
| Tsai (2023)‐13 | Low | Low | Low | High | High | |
| Tsai (2023)‐14 | Low | Low | Low | High | High | |
| Tsai (2023)‐15 | Low | Low | Low | High | High | |
| Tsai (2023)‐16 | Low | Low | Low | High | High | |
| Tsai (2023)‐17 | Low | Low | Low | High | High | |
| Tsai (2023)‐18 | Low | Low | Low | High | High | |
| Tsai (2023)‐19 | Low | Low | Low | High | High | |
| Tsai (2023)‐20 | Low | Low | Low | High | High | |
| Tsai (2023)‐21 | Low | Low | Low | High | High | |
| Tsai (2023)‐22 | Low | Low | Low | High | High | |
| Tuomikoski 2015 | Tuomikoski (2015)‐1 | Low | Low | Low | High | High |
| Van den Bosch 2021 | Van den Bosch (2021)‐1 | Low | Low | Low | Low | Low |
| Van den Bosch (2021)‐2 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐3 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐4 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐5 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐6 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐7 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐8 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐9 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐10 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐11 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐12 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐13 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐14 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐15 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐16 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐17 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐18 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐19 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐20 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐21 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐22 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐23 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐24 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐25 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐26 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐27 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐28 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐29 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐30 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐31 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐32 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐33 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐34 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐35 | Low | Low | Low | Low | Low | |
| Van den Bosch (2021)‐36 | Low | Low | Low | Low | Low | |
| Van Rijn‐Dekker 2023 | van Rijn‐Dekker (2023)‐1 | Low | Low | Low | Low | Low |
| van Rijn‐Dekker (2023)‐2 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐3 | Low | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐4 | Low | Low | Low | High | High | |
| van Rijn‐Dekker (2023)‐5 | Low | Low | Low | High | High | |
| van Rijn‐Dekker (2023)‐6 | Low | Low | Low | High | High | |
| van Rijn‐Dekker (2023)‐7 | Low | Low | Low | High | High | |
| van Rijn‐Dekker (2023)‐8 | Low | Low | Low | High | High | |
| van Rijn‐Dekker (2023)‐9 | Low | Low | Low | High | High | |
| Vidyasagar 2020 | Vidyasagar (2020)‐1 | Low | Low | Low | High | High |
| Wilkie 2020 | Wilkie (2020)‐1 | Low | Low | Low | High | High |
| Wilkie (2020)‐2 | Low | Low | Low | High | High | |
| Willemsen 2022 | Willemsen (2022)‐1 | Low | Unclear | Low | Low | Unclear |
| Wongwattananard 2023 | Wongwattananard (2023)‐1 | Low | Unclear | Low | High | High |
| Wopken 2014a | Wopken (2014a)‐1 | Low | Low | Low | Unclear | Unclear |
| Yang 2023a | Yang (2023a)‐1 | Low | Low | Low | High | High |
| Yang (2023a)‐2 | Low | Low | Low | High | High | |
| Yang (2023a)‐3 | Low | Low | Low | High | High | |
| Yang (2023a)‐4 | Low | Low | Low | High | High | |
| Yang (2023a)‐5 | Low | Low | Low | High | High | |
| Yang (2023a)‐6 | Low | Low | Low | High | High | |
| Yang 2023b | Yang (2023b)‐1 | Low | Low | Low | Low | Low |
| Zhu 2021 | Zhu (2021)‐1 | Low | Low | High | Unclear | High |
| Zhu (2021)‐2 | Low | Low | Low | Unclear | Unclear | |
| Zhu (2021)‐3 | Low | Low | Low | Unclear | Unclear | |
| Zhu (2021)‐4 | Low | Low | Low | Unclear | Unclear | |
| Zhu 2024 | Zhu (2024)‐1 | Unclear | Low | Unclear | High | High |
| Zhu (2024)‐2 | Unclear | Low | Unclear | High | High | |
| Zhu (2024)‐3 | Unclear | Low | Unclear | High | High | |
| Zhu (2024)‐4 | Unclear | Low | Unclear | High | High | |
Appendix 8. Assessment of applicability concerns of external validation studies (n = 193) from 39 publications
| Publication | Study subID | PROBAST ‐ Applicability | |||
| Participants Domain | Predictors Domain | Outcome Domain | Overall Judgement | ||
| Beetz 2012c | Beetz (2012c)‐1 | Low | Low | Low | Low |
| Beetz (2012c)‐2 | Low | Low | Low | Low | |
| Blanchard 2016 | Blanchard (2016)‐1 | Low | Low | Low | Low |
| Blanchard (2016)‐2 | Low | Low | Low | Low | |
| Blanchard (2016)‐3 | Low | Low | Low | Low | |
| Blanchard (2016)‐4 | Low | Low | Low | Low | |
| Blanchard (2016)‐5 | Low | Low | Low | Low | |
| Buettner 2012 | Buettner (2012)‐1 | Unclear | Low | Low | Unclear |
| Buettner (2012)‐2 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐3 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐4 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐5 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐6 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐7 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐8 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐9 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐10 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐11 | Low | Low | Low | Low | |
| Buettner (2012)‐12 | Low | Low | Low | Low | |
| Buettner (2012)‐13 | Low | Low | Low | Low | |
| Buettner (2012)‐14 | Low | Low | Low | Low | |
| Buettner (2012)‐15 | Low | Low | Low | Low | |
| Buettner (2012)‐16 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐17 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐18 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐19 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐20 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐21 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐22 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐23 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐24 | Unclear | Low | Low | Unclear | |
| Buettner (2012)‐25 | Unclear | Low | Low | Unclear | |
| Cavallo 2021 | Cavallo (2021)‐1 | Unclear | Low | Low | Unclear |
| Cavallo (2021)‐2 | Unclear | Low | Low | Unclear | |
| Chao 2022 | Chao (2022)‐1 | Unclear | Low | Low | Unclear |
| Chao (2022)‐2 | Unclear | Low | Low | Unclear | |
| Chao (2022)‐3 | Unclear | Low | Low | Unclear | |
| Chao (2022)‐4 | Unclear | Low | Low | Unclear | |
| Christianen 2016 | Christianen (2016)‐1 | Low | Low | Low | Low |
| Dean 2018 | Dean (2018)‐1 | Unclear | Low | Unclear | Unclear |
| Dean (2018)‐2 | Unclear | Low | Unclear | Unclear | |
| Dean (2018)‐3 | Unclear | Low | Unclear | Unclear | |
| Dean (2018)‐4 | Unclear | Low | Unclear | Unclear | |
| Dean (2018)‐5 | Unclear | Low | Unclear | Unclear | |
| Dean (2018)‐6 | Unclear | Low | Unclear | Unclear | |
| Deneuve 2023 | Deneuve (2023)‐1 | Unclear | Low | Low | Unclear |
| Deneuve (2023)‐2 | Unclear | Low | Low | Unclear | |
| Deneuve (2023)‐3 | Unclear | Low | Low | Unclear | |
| Deneuve (2023)‐4 | Unclear | Low | Low | Unclear | |
| Deutsch 2021 | Deutsch (2020)‐1 | Low | Low | Low | Low |
| Hansen 2019 | Hansen (2019)‐1 | Unclear | Low | Low | Unclear |
| Huynh 2023 | Huynh (2023)‐1 | Low | Low | Low | Low |
| Huynh (2023)‐2 | Low | Low | Low | Low | |
| Huynh (2023)‐3 | Low | Low | Low | Low | |
| Huynh (2023)‐4 | Low | Low | Low | Low | |
| Huynh (2023)‐5 | Low | Low | Low | Low | |
| Huynh (2023)‐6 | Low | Low | Low | Low | |
| Kalendralis 2022 | Kalendralis (2022)‐1 | Low | Low | Low | Low |
| Kamal 2020 | Kamal (2020)‐1 | Low | Low | High | High |
| Kamal (2020)‐2 | Low | Low | High | High | |
| Kanayama 2018 | Kanayama (2018)‐1 | Low | Low | Low | Low |
| Kinclova 2020 | Kinclova (2020)‐1 | Low | Low | Low | Low |
| Kinclova (2020)‐2 | Low | Low | Low | Low | |
| Kinclova (2020)‐3 | Low | Low | Low | Low | |
| Koiwai 2010 | Koiwai (2010)‐1 | Low | Low | High | High |
| Koiwai (2010)‐2 | Low | Low | High | High | |
| Langendijk 2021 | Langendijk‐2021‐1 | Low | Low | Low | Low |
| Langendijk‐2021‐2 | Low | Low | Low | Low | |
| Langendijk‐2021‐3 | Low | Low | Low | Low | |
| Langendijk‐2021‐4 | Low | Low | Low | Low | |
| Langendijk‐2021‐5 | Low | Low | Low | Low | |
| Langendijk‐2021‐6 | Low | Low | Low | Low | |
| Lee 2014b | Lee (2014b)‐1 | Low | Low | Low | Low |
| Lee (2014b)‐2 | Low | Low | Low | Low | |
| Lee (2014b)‐3 | Low | Low | Low | Low | |
| Lee (2014b)‐4 | Low | Low | Low | Low | |
| Li 2023 | Li (2023)‐1 | Low | Low | Low | Low |
| Li (2023)‐2 | Low | Low | Low | Low | |
| Li (2023)‐3 | Low | Low | Low | Low | |
| Luo 2018 | Luo (2018)‐1 | Low | Low | Low | Low |
| Luo (2018)‐2 | Low | Low | Low | Low | |
| Luo (2018)‐3 | Low | Low | Low | Low | |
| Luo (2018)‐4 | Low | Low | Low | Low | |
| Nevens 2016 | Nevens (2016)‐1 | Low | Low | Low | Low |
| Nevens (2016)‐2 | Low | Low | Low | Low | |
| Nevens (2016)‐3 | Low | Low | Low | Low | |
| Nevens (2016)‐4 | Low | Low | Low | Low | |
| Nevens (2016)‐5 | Low | Low | Low | Low | |
| Nowicka 2020 | Nowicka (2020)‐1 | Low | Low | Low | Low |
| Nowicka (2020)‐2 | Low | Low | Low | Low | |
| Nowicka (2020)‐3 | Low | Low | Low | Low | |
| Nowicka (2020)‐4 | Low | Low | Low | Low | |
| Nowicka (2020)‐5 | Low | Low | Low | Low | |
| OuYang 2023 | OuYang (2023)‐1 | Low | Low | Low | Low |
| OuYang (2023)‐2 | Low | Low | Low | Low | |
| OuYang (2023)‐3 | Low | Low | Low | Low | |
| OuYang (2023)‐4 | Low | Low | Low | Low | |
| Ronjom 2015 | Ronjom (2015)‐1 | Low | Low | Low | Low |
| Sharabiani 2021 | Sharabiani (2021)‐1 | Low | Low | Low | Low |
| Sharabiani (2021)‐2 | Low | Low | Low | Low | |
| Sharabiani (2021)‐3 | Low | Low | Low | Low | |
| Sharabiani (2021)‐4 | Low | Low | Low | Low | |
| Sharabiani (2021)‐5 | Low | Low | Low | Low | |
| Thor 2017 | Thor (2017)‐1 | Low | Low | Low | Low |
| Tsai 2023 | Tsai (2023)‐1 | Low | Low | Low | Low |
| Tsai (2023)‐2 | Low | Low | Low | Low | |
| Tsai (2023)‐3 | Low | Low | Low | Low | |
| Tsai (2023)‐4 | Low | Low | Low | Low | |
| Tsai (2023)‐5 | Low | Low | Low | Low | |
| Tsai (2023)‐6 | Low | Low | Low | Low | |
| Tsai (2023)‐7 | Low | Low | Low | Low | |
| Tsai (2023)‐8 | Low | Low | Low | Low | |
| Tsai (2023)‐9 | Low | Low | Low | Low | |
| Tsai (2023)‐10 | Low | Low | Low | Low | |
| Tsai (2023)‐11 | Low | Low | Low | Low | |
| Tsai (2023)‐12 | Low | Low | Low | Low | |
| Tsai (2023)‐13 | Low | Low | Low | Low | |
| Tsai (2023)‐14 | Low | Low | Low | Low | |
| Tsai (2023)‐15 | Low | Low | Low | Low | |
| Tsai (2023)‐16 | Low | Low | Low | Low | |
| Tsai (2023)‐17 | Low | Low | Low | Low | |
| Tsai (2023)‐18 | Low | Low | Low | Low | |
| Tsai (2023)‐19 | Low | Low | Low | Low | |
| Tsai (2023)‐20 | Low | Low | Low | Low | |
| Tsai (2023)‐21 | Low | Low | Low | Low | |
| Tsai (2023)‐22 | Low | Low | Low | Low | |
| Tuomikoski 2015 | Tuomikoski (2015)‐1 | Low | Low | Low | Low |
| Van den Bosch 2021 | Van den Bosch (2021)‐1 | Low | Low | Low | Low |
| Van den Bosch (2021)‐2 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐3 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐4 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐5 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐6 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐7 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐8 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐9 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐10 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐11 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐12 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐13 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐14 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐15 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐16 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐17 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐18 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐19 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐20 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐21 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐22 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐23 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐24 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐25 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐26 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐27 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐28 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐29 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐30 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐31 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐32 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐33 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐34 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐35 | Low | Low | Low | Low | |
| Van den Bosch (2021)‐36 | Low | Low | Low | Low | |
| Van Rijn‐Dekker 2023 | van Rijn‐Dekker (2023)‐1 | Low | Low | Low | Low |
| van Rijn‐Dekker (2023)‐2 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐3 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐4 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐5 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐6 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐7 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐8 | Low | Low | Low | Low | |
| van Rijn‐Dekker (2023)‐9 | Low | Low | Low | Low | |
| Vidyasagar 2020 | Vidyasagar (2020)‐1 | Low | Low | Low | Low |
| Wilkie 2020 | Wilkie (2020)‐1 | Low | Low | Low | Low |
| Wilkie (2020)‐2 | Low | Low | Low | Low | |
| Willemsen 2022 | Willemsen (2022)‐1 | Low | Low | Low | Low |
| Wongwattananard 2023 | Wongwattananard (2023)‐1 | Low | Low | Low | Low |
| Wopken 2014a | Wopken (2014a)‐1 | Low | Low | Low | Low |
| Yang 2023a | Yang (2023a)‐1 | Unclear | Low | Low | Unclear |
| Yang (2023a)‐2 | Unclear | Low | Low | Unclear | |
| Yang (2023a)‐3 | Unclear | Low | Low | Unclear | |
| Yang (2023a)‐4 | Unclear | Low | Low | Unclear | |
| Yang (2023a)‐5 | Unclear | Low | Low | Unclear | |
| Yang (2023a)‐6 | Unclear | Low | Low | Unclear | |
| Yang 2023b | Yang (2023b)‐1 | Low | Low | Low | Low |
| Zhu 2021 | Zhu (2021)‐1 | Low | Low | Low | Low |
| Zhu (2021)‐2 | Low | Low | Low | Low | |
| Zhu (2021)‐3 | Low | Low | Low | Low | |
| Zhu (2021)‐4 | Low | Low | Low | Low | |
| Zhu 2024 | Zhu (2024)‐1 | Unclear | Low | Unclear | Unclear |
| Zhu (2024)‐2 | Unclear | Low | Unclear | Unclear | |
| Zhu (2024)‐3 | Unclear | Low | Unclear | Unclear | |
| Zhu (2024)‐4 | Unclear | Low | Unclear | Unclear | |
Characteristics of studies
Characteristics of included studies [ordered by study ID]
Abdollahi 2023.
| Study characteristics | ||
| Notes | ||
Alterio 2017.
| Study characteristics | ||
| Notes | ||
Anderson 2018.
| Study characteristics | ||
| Notes | ||
Bakhshandeh 2013.
| Study characteristics | ||
| Notes | ||
Beetz 2012a.
| Study characteristics | ||
| Notes | ||
Beetz 2012b.
| Study characteristics | ||
| Notes | ||
Beetz 2012c.
| Study characteristics | ||
| Notes | ||
Bhide 2012.
| Study characteristics | ||
| Notes | ||
Bin 2022.
| Study characteristics | ||
| Notes | ||
Blanchard 2016.
| Study characteristics | ||
| Notes | ||
Blanco 2005.
| Study characteristics | ||
| Notes | ||
Boomsma 2012.
| Study characteristics | ||
| Notes | ||
Buettner 2010.
| Study characteristics | ||
| Notes | ||
Buettner 2012.
| Study characteristics | ||
| Notes | ||
Busato 2023.
| Study characteristics | ||
| Notes | ||
Cavallo 2021.
| Study characteristics | ||
| Notes | ||
Chao 2001.
| Study characteristics | ||
| Notes | ||
Chao 2022.
| Study characteristics | ||
| Notes | ||
Chen 2013.
| Study characteristics | ||
| Notes | ||
Cheng 2018.
| Study characteristics | ||
| Notes | ||
Cheng 2019.
| Study characteristics | ||
| Notes | ||
Cheraghi 2017.
| Study characteristics | ||
| Notes | ||
Chow 2019.
| Study characteristics | ||
| Notes | ||
Christianen 2012.
| Study characteristics | ||
| Notes | ||
Christianen 2016.
| Study characteristics | ||
| Notes | ||
Dale 2016.
| Study characteristics | ||
| Notes | ||
Dean 2016.
| Study characteristics | ||
| Notes | ||
Dean 2018.
| Study characteristics | ||
| Notes | ||
Deneuve 2023.
| Study characteristics | ||
| Notes | ||
Deutsch 2021.
| Study characteristics | ||
| Notes | ||
Dijkema 2010.
| Study characteristics | ||
| Notes | ||
Dohopolski 2022.
| Study characteristics | ||
| Notes | ||
Dong 2023.
| Study characteristics | ||
| Notes | ||
Eisbruch 1999.
| Study characteristics | ||
| Notes | ||
Eisbruch 2011.
| Study characteristics | ||
| Notes | ||
Fanizzi 2022.
| Study characteristics | ||
| Notes | ||
Guan 2020.
| Study characteristics | ||
| Notes | ||
Gupta 2015.
| Study characteristics | ||
| Notes | ||
Hamada 2023.
| Study characteristics | ||
| Notes | ||
Hansen 2019.
| Study characteristics | ||
| Notes | ||
Hansen 2020.
| Study characteristics | ||
| Notes | ||
He 2023.
| Study characteristics | ||
| Notes | ||
He 2024.
| Study characteristics | ||
| Notes | ||
Hosseinian 2023.
| Study characteristics | ||
| Notes | ||
Houweling 2010.
| Study characteristics | ||
| Notes | ||
Huang 2022.
| Study characteristics | ||
| Notes | ||
Humbert‐Vidan 2021.
| Study characteristics | ||
| Notes | ||
Humbert‐Vidan 2022.
| Study characteristics | ||
| Notes | ||
Huynh 2023.
| Study characteristics | ||
| Notes | ||
Kalendralis 2022.
| Study characteristics | ||
| Notes | ||
Kamal 2020.
| Study characteristics | ||
| Notes | ||
Kamstra 2015.
| Study characteristics | ||
| Notes | ||
Kanayama 2018.
| Study characteristics | ||
| Notes | ||
Kanehira 2021.
| Study characteristics | ||
| Notes | ||
Karsten 2019.
| Study characteristics | ||
| Notes | ||
Kawamura 2019.
| Study characteristics | ||
| Notes | ||
Kinclova 2020.
| Study characteristics | ||
| Notes | ||
Koiwai 2010.
| Study characteristics | ||
| Notes | ||
Kraaijenga 2019.
| Study characteristics | ||
| Notes | ||
Krasin 2012.
| Study characteristics | ||
| Notes | ||
Langendijk 2009.
| Study characteristics | ||
| Notes | ||
Langendijk 2021.
| Study characteristics | ||
| Notes | ||
Langius 2016.
| Study characteristics | ||
| Notes | ||
Lee 2012.
| Study characteristics | ||
| Notes | ||
Lee 2014a.
| Study characteristics | ||
| Notes | ||
Lee 2014b.
| Study characteristics | ||
| Notes | ||
Lee 2015a.
| Study characteristics | ||
| Notes | ||
Lee 2015b.
| Study characteristics | ||
| Notes | ||
Lee 2023.
| Study characteristics | ||
| Notes | ||
Lescut 2013.
| Study characteristics | ||
| Notes | ||
Li 2020.
| Study characteristics | ||
| Notes | ||
Li 2022a.
| Study characteristics | ||
| Notes | ||
Li 2022b.
| Study characteristics | ||
| Notes | ||
Li 2023.
| Study characteristics | ||
| Notes | ||
Lindblom 2014.
| Study characteristics | ||
| Notes | ||
Ling 2020.
| Study characteristics | ||
| Notes | ||
Liu 2019.
| Study characteristics | ||
| Notes | ||
Liu 2022.
| Study characteristics | ||
| Notes | ||
Luo 2017.
| Study characteristics | ||
| Notes | ||
Luo 2018.
| Study characteristics | ||
| Notes | ||
Marzi 2009.
| Study characteristics | ||
| Notes | ||
Marzi 2021.
| Study characteristics | ||
| Notes | ||
Mavroidis 2003.
| Study characteristics | ||
| Notes | ||
Mavroidis 2017.
| Study characteristics | ||
| Notes | ||
Mavroidis 2018.
| Study characteristics | ||
| Notes | ||
Men 2019.
| Study characteristics | ||
| Notes | ||
Mori 2019.
| Study characteristics | ||
| Notes | ||
Morimoto 2019.
| Study characteristics | ||
| Notes | ||
Murthy 2018.
| Study characteristics | ||
| Notes | ||
Musha 2020.
| Study characteristics | ||
| Notes | ||
Nevens 2016.
| Study characteristics | ||
| Notes | ||
Nourissat 2010.
| Study characteristics | ||
| Notes | ||
Nowicka 2020.
| Study characteristics | ||
| Notes | ||
Onjukka 2020.
| Study characteristics | ||
| Notes | ||
Orlandi 2018.
| Study characteristics | ||
| Notes | ||
OuYang 2023.
| Study characteristics | ||
| Notes | ||
Padannayil 2023.
| Study characteristics | ||
| Notes | ||
Pan 2020a.
| Study characteristics | ||
| Notes | ||
Pan 2020b.
| Study characteristics | ||
| Notes | ||
Peng 2020.
| Study characteristics | ||
| Notes | ||
Peuker 2022.
| Study characteristics | ||
| Notes | ||
Pota 2017.
| Study characteristics | ||
| Notes | ||
Qin 2023.
| Study characteristics | ||
| Notes | ||
Rachi 2023.
| Study characteristics | ||
| Notes | ||
Rades 2022.
| Study characteristics | ||
| Notes | ||
Rancati 2009.
| Study characteristics | ||
| Notes | ||
Rao 2016.
| Study characteristics | ||
| Notes | ||
Ren 2021.
| Study characteristics | ||
| Notes | ||
Renda 2020.
| Study characteristics | ||
| Notes | ||
Ritlumlert 2023.
| Study characteristics | ||
| Notes | ||
Ronjom 2013.
| Study characteristics | ||
| Notes | ||
Ronjom 2015.
| Study characteristics | ||
| Notes | ||
Rosen 2018.
| Study characteristics | ||
| Notes | ||
Rwigema 2019.
| Study characteristics | ||
| Notes | ||
Schuette 2020.
| Study characteristics | ||
| Notes | ||
Scrimger 2004.
| Study characteristics | ||
| Notes | ||
Sharabiani 2021.
| Study characteristics | ||
| Notes | ||
Sheikh 2019.
| Study characteristics | ||
| Notes | ||
Shen 2021.
| Study characteristics | ||
| Notes | ||
Sheu 2014.
| Study characteristics | ||
| Notes | ||
Shirai 2017.
| Study characteristics | ||
| Notes | ||
Shorter 2017.
| Study characteristics | ||
| Notes | ||
Sijtsema 2023.
| Study characteristics | ||
| Notes | ||
Singh 2023.
| Study characteristics | ||
| Notes | ||
Singla 2021.
| Study characteristics | ||
| Notes | ||
Soares 2014.
| Study characteristics | ||
| Notes | ||
Soares 2018.
| Study characteristics | ||
| Notes | ||
Soderstrom 2017.
| Study characteristics | ||
| Notes | ||
Somay 2023.
| Study characteristics | ||
| Notes | ||
Suresh 2010.
| Study characteristics | ||
| Notes | ||
Teguh 2013.
| Study characteristics | ||
| Notes | ||
Teng 2021.
| Study characteristics | ||
| Notes | ||
Tenhunen 2008.
| Study characteristics | ||
| Notes | ||
Theunissen 2015.
| Study characteristics | ||
| Notes | ||
Thor 2017.
| Study characteristics | ||
| Notes | ||
Tomasik 2021.
| Study characteristics | ||
| Notes | ||
Tsai 2017.
| Study characteristics | ||
| Notes | ||
Tsai 2023.
| Study characteristics | ||
| Notes | ||
Tuomikoski 2015.
| Study characteristics | ||
| Notes | ||
Tzikas 2023.
| Study characteristics | ||
| Notes | ||
Ursino 2020.
| Study characteristics | ||
| Notes | ||
Van den Bosch 2021.
| Study characteristics | ||
| Notes | ||
Van Dijk 2021.
| Study characteristics | ||
| Notes | ||
Van Rijn‐Dekker 2023.
| Study characteristics | ||
| Notes | ||
Verduijn 2023.
| Study characteristics | ||
| Notes | ||
Vidyasagar 2020.
| Study characteristics | ||
| Notes | ||
Wang 2019.
| Study characteristics | ||
| Notes | ||
Wang 2020.
| Study characteristics | ||
| Notes | ||
Wang 2022.
| Study characteristics | ||
| Notes | ||
Wang 2024.
| Study characteristics | ||
| Notes | ||
Ward 2019.
| Study characteristics | ||
| Notes | ||
Wen 2021.
| Study characteristics | ||
| Notes | ||
Wentzel 2023.
| Study characteristics | ||
| Notes | ||
Werbrouck 2009.
| Study characteristics | ||
| Notes | ||
Wilkie 2020.
| Study characteristics | ||
| Notes | ||
Willemsen 2020.
| Study characteristics | ||
| Notes | ||
Willemsen 2022.
| Study characteristics | ||
| Notes | ||
Wongwattananard 2023.
| Study characteristics | ||
| Notes | ||
Wopken 2014a.
| Study characteristics | ||
| Notes | ||
Wopken 2014b.
| Study characteristics | ||
| Notes | ||
Wu 2019.
| Study characteristics | ||
| Notes | ||
Wu 2022.
| Study characteristics | ||
| Notes | ||
Yahya 2022.
| Study characteristics | ||
| Notes | ||
Yan 2021.
| Study characteristics | ||
| Notes | ||
Yang 2021.
| Study characteristics | ||
| Notes | ||
Yang 2023a.
| Study characteristics | ||
| Notes | ||
Yang 2023b.
| Study characteristics | ||
| Notes | ||
Yu 2016.
| Study characteristics | ||
| Notes | ||
Zeng 2015.
| Study characteristics | ||
| Notes | ||
Zhou 2020.
| Study characteristics | ||
| Notes | ||
Zhu 2021.
| Study characteristics | ||
| Notes | ||
Zhu 2024.
| Study characteristics | ||
| Notes | ||
Characteristics of excluded studies [ordered by study ID]
| Study | Reason for exclusion |
|---|---|
| Aarup‐Kristensen 2019 | No prediction model reported |
| Abdollahi 2018 | Ineligible domain |
| Adenis 2013 | No prediction model reported |
| Agarwal 2009 | No prediction model reported |
| Agarwalla 2015 | No prediction model reported |
| Agren 1990 | Ineligible domain |
| Ahamed 2016 | Conference abstract |
| Ahmed 2019 | Conference abstract |
| Akagunduz 2022 | No prediction model reported |
| Akashi 2018 | No prediction model reported |
| Akgun 2014 | No prediction model reported |
| Alevronta 2010 | Non‐nested case‐control study design |
| Alexidis 2022 | No prediction model reported |
| Alexidis 2023 | No prediction model reported |
| Alicikus 2009 | No prediction model reported |
| Alizade‐Harakiyan 2020 | Ineligible domain |
| Alizade‐Harakiyan 2022 | Ineligible domain |
| Al‐Mamgani 2013 | No prediction model reported |
| Al‐Othman 2003 | No prediction model reported |
| Amiri 2023 | No prediction model reported |
| Amosson 2003a | No prediction model reported |
| Amosson 2003b | No prediction model reported |
| Anderson 2012 | Conference abstract |
| Anderson 2014 | No prediction model reported |
| Arts 2017 | Ineligible outcome |
| Astaburuaga 2019 | Predictors collected after starting treatment |
| Astreinidou 2004 | Ineligible outcome |
| Astrup 2015 | No prediction model reported |
| Augustin 2017 | No prediction model reported |
| Austin 2011 | Conference abstract |
| Aylward 2020 | Ineligible domain |
| Aziz 2016 | Ineligible domain |
| Badiyan 2012 | Conference abstract |
| Bae 2012 | No prediction model reported |
| Bair 2013 | Conference abstract |
| Bairati 2009 | Conference abstract |
| Baker 2019 | No prediction model reported |
| Bandlamudi 2018 | No prediction model reported |
| Bansal 2023 | No prediction model reported |
| Bao 2022a | Non‐nested case‐control study design |
| Bao 2022b | Non‐nested case‐control study design |
| Bao 2022c | Predictors collected after starting treatment |
| Barnhart 2017 | No prediction model reported |
| Barringer 2009 | Conference abstract |
| Barry 2014a | Conference abstract |
| Barry 2014b | Conference abstract |
| Barua 2021 | Ineligible domain |
| Basu 2019 | Conference abstract |
| Batth 2013 | No prediction model reported |
| Beasley 2018 | No prediction model reported |
| Beddok 2023 | No prediction model reported |
| Beetz 2010 | Conference abstract |
| Beetz 2013 | No prediction model reported |
| Beetz 2014 | No prediction model reported |
| Behrends 2020 | No prediction model reported |
| Belli 2014 | Predictors collected after starting treatment |
| Bentzen 1996 | No prediction model reported |
| Ben‐Yosef 1992 | No prediction model reported |
| Berger 2022 | Predictors collected after starting treatment |
| Berger 2023a | Predictors collected after starting treatment |
| Berger 2023b | Predictors collected after starting treatment |
| Beschel 2016 | No prediction model reported |
| Bhandare 2009 | Conference abstract |
| Bhandare 2012a | Conference abstract |
| Bhandare 2012b | Conference abstract |
| Bhattacharyya 2020 | No prediction model reported |
| Bhide 2009 | Conference abstract |
| Bhide 2011 | Conference abstract |
| Bianciotto 2010 | No prediction model reported |
| Bibik 2017 | Conference abstract |
| Blanchard 2016a | Conference abstract |
| Boelke 2013 | Conference abstract |
| Boguszewicz 2019 | No prediction model reported |
| Bolusani 2008 | No prediction model reported |
| Bonomo 2019 | Conference abstract |
| Bonomo 2020 | No prediction model reported |
| Boomsma 2010 | Conference abstract |
| Boomsma 2011 | Conference abstract |
| Borchiellini 2010 | Conference abstract |
| Bordon 2010a | No prediction model reported |
| Bordon 2010b | Conference abstract |
| Bossi 2013 | Conference abstract |
| Bossi 2016 | Predictors collected after starting treatment |
| Boustani 2021 | Ineligible domain |
| Bowen 2015 | No prediction model reported |
| Bozec 2016 | No prediction model reported |
| Braam 2005 | No prediction model reported |
| Bragante 2015 | No prediction model reported |
| Britton 2012 | No prediction model reported |
| Brodin 2014 | No prediction model reported |
| Brodin 2019 | No prediction model reported |
| Broggi 2012 | Conference abstract |
| Brooker 2021 | Non‐nested case‐control study design |
| Brouwer 2016 | Conference abstract |
| Brown 2011 | Not an original study |
| Brown 2013 | Ineligible domain |
| Brown 2016 | Ineligible domain |
| Brzozowska 2018a | No prediction model reported |
| Brzozowska 2018b | No prediction model reported |
| Buchapudi 2019 | No prediction model reported |
| Buettner 2011 | Conference abstract |
| Burman 1991 | Methodological focus |
| Bussels 2004 | No prediction model reported |
| Caglar 2008 | No prediction model reported |
| Cai 2019 | Ineligible domain |
| Cai 2022 | No prediction model reported |
| Canahuate 2023 | No prediction model reported |
| Cannon 2012 | No prediction model reported |
| Caparrotti 2017 | No prediction model reported |
| Caria 2010 | Conference abstract |
| Carpenter 2018 | No prediction model reported |
| Cartmill 2013 | No prediction model reported |
| Carver 2019 | Conference abstract |
| Castro 2011 | Conference abstract |
| Caudell 2010 | No prediction model reported |
| Caudell 2011a | No prediction model reported |
| Caudell 2011b | Conference abstract |
| Caudell 2018 | No prediction model reported |
| Cavalieri 2023 | No prediction model reported |
| Cavallo 2017 | Conference abstract |
| Cecatto 2015 | Ineligible domain |
| Cella 2012 | Ineligible domain |
| Cella 2013 | No prediction model reported |
| Cengiz 2016 | Conference abstract |
| Chaibakhsh 2018 | No prediction model reported |
| Chan 2009 | No prediction model reported |
| Chang 2017 | No prediction model reported |
| Chao 2002 | Ineligible outcome |
| Chao 2019a | Methodological focus |
| Chao 2019b | Conference abstract |
| Chaudhary 2014a | Conference abstract |
| Chaudhary 2014b | Conference abstract |
| Chen 2010a | No prediction model reported |
| Chen 2010b | No prediction model reported |
| Chen 2012 | No prediction model reported |
| Chen 2014a | Conference abstract |
| Chen 2014b | Conference abstract |
| Chen 2014c | No prediction model reported |
| Chen 2015 | No prediction model reported |
| Chen 2016 | No prediction model reported |
| Chen 2017 | No prediction model reported |
| Chen 2019 | Conference abstract |
| Chen 2020 | No prediction model reported |
| Chen 2023 | No prediction model reported |
| Cheng 1999 | No prediction model reported |
| Cheng 2000 | No prediction model reported |
| Cheng 2016 | Conference abstract |
| Cheng 2019a | Conference abstract |
| Chera 2016 | Conference abstract |
| Chera 2017 | No prediction model reported |
| Chilukuri 2020 | No prediction model reported |
| Cho 2019 | Ineligible domain |
| Chow 2019a | Duplicate |
| Chow 2022 | No prediction model reported |
| Christianen 2010 | Conference abstract |
| Christianen 2011a | Conference abstract |
| Christianen 2011b | Conference abstract |
| Christianen 2015a | Conference abstract |
| Christianen 2015b | No prediction model reported |
| Christopherson 2019 | No prediction model reported |
| Chyan 2014 | No prediction model reported |
| Cicinelli 2021 | Ineligible domain |
| Clark 2015 | No prediction model reported |
| Coates 2015 | Not an original study |
| Cook 2016 | Conference abstract |
| Coucke 1993 | No prediction model reported |
| Danielsson 2016 | Non‐nested case‐control study design |
| Dankers 2017 | Ineligible domain |
| De 2016 | No prediction model reported |
| Dean 2016a | Methodological focus |
| Dean 2017 | No prediction model reported |
| Deantonio 2013 | No prediction model reported |
| De Araujo 2021 | Methodological focus |
| Deasy 2001 | Methodological focus |
| De Blank 2013 | No prediction model reported |
| DeConde 2011 | Conference abstract |
| Deist 2018 | No prediction model reported |
| Delana 2009 | No prediction model reported |
| DeLuke 2022 | No prediction model reported |
| De Marzi 2015 | Ineligible domain |
| Demizu 2009 | No prediction model reported |
| Den 2019 | Conference abstract |
| Deneuve 2019 | Conference abstract |
| Denham 1996 | Ineligible domain |
| Denham 1999 | No prediction model reported |
| Deore 1993 | Ineligible domain |
| De Ruyck 2012 | Conference abstract |
| De Ruyck 2013 | Methodological focus |
| Deschuymer 2018 | No prediction model reported |
| Diaz 2010 | No prediction model reported |
| Dijkema 2008 | No prediction model reported |
| Dirix 2009 | No prediction model reported |
| Dixon 2018 | No prediction model reported |
| Dong 2016 | Conference abstract |
| Draguet 2022 | Ineligible domain |
| Driessen 2019 | No prediction model reported |
| Dunavoelgyi 2013 | Ineligible domain |
| Duru 2019 | Conference abstract |
| Dziegielewski 2020 | No prediction model reported |
| Ebrahimi 2022 | Ineligible domain |
| Eisbruch 2001 | No prediction model reported |
| Eisbruch 2003 | No prediction model reported |
| Elhalawani 2018 | Conference abstract |
| Elhalawani 2021 | Predictors collected after starting treatment |
| El‐Shebiney 2018 | No prediction model reported |
| Eneroth 1972 | No prediction model reported |
| Engeseth 2022 | Ineligible domain |
| Erkal 2014 | No prediction model reported |
| Esassolak 2004 | No prediction model reported |
| Espensen 2016 | Conference abstract |
| Espensen 2019 | No prediction model reported |
| Espensen 2021 | Ineligible domain |
| Estilo 2012 | Conference abstract |
| Facchinetti 2017 | Conference abstract |
| Famoso 2014 | Conference abstract |
| Fan 2017 | No prediction model reported |
| Fan 2018 | No prediction model reported |
| Fan 2021 | No prediction model reported |
| Fang 2022 | No prediction model reported |
| Farias 2003 | No prediction model reported |
| Feen 2016 | Not an original study |
| Feng 2018 | No prediction model reported |
| Fiore 2018 | Conference abstract |
| Fiorino 2011 | Conference abstract |
| Flores 2018 | Conference abstract |
| Fogliata 2017 | No prediction model reported |
| Foss 1997 | No prediction model reported |
| Fossum 2016 | Conference abstract |
| Fossum 2017 | No prediction model reported |
| Fowler 2003 | Not an original study |
| Francis 2017 | No prediction model reported |
| Frowen 2010 | No prediction model reported |
| Frowen 2013 | No prediction model reported |
| Frowen 2015 | Conference abstract |
| Fu 1995 | No prediction model reported |
| Fuccio 2016 | Ineligible domain |
| Fukada 2012 | No prediction model reported |
| Fukada 2013 | No prediction model reported |
| Fukada 2021 | Ineligible domain |
| Gabrys 2017 | No prediction model reported |
| Gabrys 2018 | No prediction model reported |
| Gangopadhyay 2009 | Conference abstract |
| Garber 2020 | No prediction model reported |
| Gawryszuk 2019 | Conference abstract |
| Gawryszuk 2021 | Methodological focus |
| Geiger 2014 | No prediction model reported |
| Gensheimer 2016 | No prediction model reported |
| Gfmduz 1999 | No prediction model reported |
| Ghadjar 2011 | Conference abstract |
| Gharzai 2019 | No prediction model reported |
| Gharzai 2020 | Conference abstract |
| Ghazali 2012 | Conference abstract |
| Ghogomu 2010 | No prediction model reported |
| Gigliotti 2016 | Conference abstract |
| Gigliotti 2018 | Ineligible domain |
| Goeleven 2017 | Conference abstract |
| Gokhale 2010 | No prediction model reported |
| Goleń 2007 | No prediction model reported |
| Gonzalez‐Garcia 2009 | No prediction model reported |
| Goutham 2012 | No prediction model reported |
| Goy 2011 | Conference abstract |
| Gragoudas 2002 | Ineligible domain |
| Grande 1992 | No prediction model reported |
| Grau 1991 | No prediction model reported |
| Guerrero 2007 | Ineligible domain |
| Gujral 2016 | No prediction model reported |
| Gulliford 2011 | Conference abstract |
| Gündüz 1999 | No prediction model reported |
| Guo 2019 | Ineligible outcome |
| Gupta 2011 | Not an original study |
| Gupta 2013 | Conference abstract |
| Gupta 2017 | Conference abstract |
| Haddy 2009 | Ineligible domain |
| Hamming‐Vrieze 2017 | Conference abstract |
| Han 2018 | No prediction model reported |
| Han 2019 | No prediction model reported |
| Han 2020 | No prediction model reported |
| Hart 2008 | Ineligible domain |
| Hartley 2011 | No prediction model reported |
| Hawkins 2017 | Conference abstract |
| He 2014 | No prediction model reported |
| Healy 2018 | Conference abstract |
| Hedman 2009 | Ineligible outcome |
| Hedström 2019a | No prediction model reported |
| Hedström 2019b | Conference abstract |
| Herbert 1981 | No prediction model reported |
| Hernandez 2016 | Conference abstract |
| Hirata 2016a | Conference abstract |
| Hirata 2016b | Conference abstract |
| Ho 2008 | No prediction model reported |
| Holmberg 2002 | No prediction model reported |
| Honore 2002 | Radiotherapy technique outdated |
| Hou 2022 | Predictors collected after starting treatment |
| Houghton 2010 | Conference abstract |
| Hsieh 2014 | Predictors collected after starting treatment |
| Huang 2012 | Conference abstract |
| Huang 2016 | No prediction model reported |
| Huang 2017 | Ineligible domain |
| Huang 2018 | Ineligible outcome |
| Huang 2019 | No prediction model reported |
| Huang 2020 | Ineligible domain |
| Huang 2020a | Ineligible domain |
| Huang 2023 | Predictors collected after starting treatment |
| Hui 2020 | Ineligible outcome |
| Huiskamp 2020 | No prediction model reported |
| Humbert‐Vidan 2017 | Conference abstract |
| Humbert‐Vidan 2019 | Conference abstract |
| Hunter 2012a | Conference abstract |
| Hunter 2012b | Conference abstract |
| Hunter 2014a | No prediction model reported |
| Hunter 2014b | Predictors collected after starting treatment |
| Hutcheson 2008 | No prediction model reported |
| Hutcheson 2013 | Conference abstract |
| Iacovelli 2015 | Conference abstract |
| Inokuchi 2012 | Conference abstract |
| Jackson 2010 | Conference abstract |
| Jackson 2011 | Conference abstract |
| Jackson 2012 | Conference abstract |
| Jakobi 2015 | No prediction model reported |
| Jellema 2005 | No prediction model reported |
| Jen 2006 | No prediction model reported |
| Jensen 2005 | No prediction model reported |
| Jensen 2007 | No prediction model reported |
| Jensen 2022 | Ineligible outcome |
| Jiang 2018 | No prediction model reported |
| Jiang 2019 | No prediction model reported |
| Jin 2020 | No prediction model reported |
| Jomaa 2018 | Conference abstract |
| Jumeau 2018 | No prediction model reported |
| Kaae 2019 | No prediction model reported |
| Kalendralis 2019 | Conference abstract |
| Kamal 2018 | No prediction model reported |
| Kamal 2019 | No prediction model reported |
| Kang 2016 | No prediction model reported |
| Karavolia 2021 | No prediction model reported |
| Karavolia 2022 | No prediction model reported |
| Kawai 2017 | No prediction model reported |
| Kazmierska 2020 | No prediction model reported |
| Khan 2011 | Conference abstract |
| Khan 2012 | Ineligible domain |
| Kierkels 2012 | Conference abstract |
| Kierkels 2019 | No prediction model reported |
| Kim 2007 | No prediction model reported |
| Kim 2014a | No prediction model reported |
| Kim 2014b | No prediction model reported |
| Kim 2020 | No prediction model reported |
| Kimura 2019 | Conference abstract |
| Kimura 2020 | No prediction model reported |
| Kin‐Fong 2009 | Conference abstract |
| Klinghammer 2010 | No prediction model reported |
| Kocak 2014 | Conference abstract |
| Koiwai 2010b | Duplicate |
| Koiwai 2011 | Not an original study |
| Kok 2019 | Ineligible domain |
| Kong 2016 | No prediction model reported |
| Kong 2018 | No prediction model reported |
| Kono 2017 | No prediction model reported |
| Kotevski 2023 | Ineligible outcome |
| Kothe 2021a | Ineligible domain |
| Kothe 2021b | Ineligible domain |
| Kovalchik 2013 | Ineligible domain |
| Krisciunas 2012 | Conference abstract |
| Ku 2022 | Ineligible domain |
| Kubo 2019 | No prediction model reported |
| Kubrak 2013 | No prediction model reported |
| Kuhnt 2005 | No prediction model reported |
| Kuhnt 2016 | No prediction model reported |
| Kumar 2014 | No prediction model reported |
| Kumar 2018 | Conference abstract |
| Kumpulainen 2000 | No prediction model reported |
| Kusaka 2022 | No prediction model reported |
| Kut 2023 | Ineligible outcome |
| Kuten 1996 | No prediction model reported |
| Kwon 2016 | Ineligible domain |
| Kwong 1996 | No prediction model reported |
| Laidley 2017 | Conference abstract |
| Laidley 2018 | No prediction model reported |
| Lakshminarayanan 2017 | Conference abstract |
| Langendijk 2011a | Not an original study |
| Langendijk 2011b | Conference abstract |
| Langendijk 2011c | Conference abstract |
| Langendijk 2013 | Conference abstract |
| Langius 2010a | No prediction model reported |
| Langius 2010b | Duplicate |
| Laskar 2016 | Conference abstract |
| Lavo 2017 | No prediction model reported |
| Lawson 2009 | No prediction model reported |
| Lazzari 2019 | Conference abstract |
| Le 2017 | No prediction model reported |
| Le 2023 | Conference abstract |
| Lee 1998 | Ineligible domain |
| Lee 2012a | No prediction model reported |
| Lee 2012b | No prediction model reported |
| Lee 2013 | No prediction model reported |
| Lee 2015c | No prediction model reported |
| Lee 2016a | No prediction model reported |
| Lee 2016b | No prediction model reported |
| Lee 2019a | No prediction model reported |
| Lee 2019b | Conference abstract |
| Lee 2021 | Ineligible outcome |
| Leng 2019 | No prediction model reported |
| Lertbutsayanukul 2018 | No prediction model reported |
| Lescut 2012 | Conference abstract |
| Leslie 1994 | No prediction model reported |
| Leu 2018 | Conference abstract |
| Leung 2005 | No prediction model reported |
| Levendag 2007 | No prediction model reported |
| Leventhal 2022 | Ineligible domain |
| Li 2007 | No prediction model reported |
| Li 2009 | No prediction model reported |
| Li 2013 | No prediction model reported |
| Li 2017a | No prediction model reported |
| Li 2017b | Conference abstract |
| Li 2017c | No prediction model reported |
| Li 2018 | No prediction model reported |
| Li 2019a | Ineligible domain |
| Li 2019b | No prediction model reported |
| Li 2019c | Conference abstract |
| Li 2022c | Ineligible domain |
| Liang 2020 | No prediction model reported |
| Lin 2018a | No prediction model reported |
| Lin 2018b | Conference abstract |
| Lin 2022 | Predictors collected after starting treatment |
| Lin 2023 | Predictors collected after starting treatment |
| Ling 2016 | Conference abstract |
| Little 2010 | Conference abstract |
| Little 2012 | No prediction model reported |
| Liu 2018 | Predictors collected after starting treatment |
| Liu 2021 | Conference abstract |
| Liu 2022a | Ineligible domain |
| Liu 2023 | Predictors collected after starting treatment |
| Lonbro 2016 | No prediction model reported |
| Lu 2018 | No prediction model reported |
| Lu 2023 | Methodological focus |
| Lumbroso 2002 | No prediction model reported |
| Lumbroso‐Le 2004 | No prediction model reported |
| Lundell 1994 | Ineligible domain |
| Luo 2005a | Duplicate |
| Luo 2005b | No prediction model reported |
| Luo 2021 | Ineligible outcome |
| Machtay 2012 | No prediction model reported |
| Maes 2002 | No prediction model reported |
| Mallick 2013 | No prediction model reported |
| Mangar 2006 | Ineligible outcome |
| Mao 2015 | Conference abstract |
| Mark 2015 | Conference abstract |
| Marks 1981 | No prediction model reported |
| Martel 1997 | Radiotherapy technique outdated |
| Maslennikova 2011 | Conference abstract |
| Matuschek 2016 | Ineligible outcome |
| Mavroidis 2004 | Methodological focus |
| Mavroidis 2017a | Conference abstract |
| Mavroidis 2019 | Conference abstract |
| Mayo 2020 | No prediction model reported |
| MD Anderson 2016a | No prediction model reported |
| MD Anderson 2016b | No prediction model reported |
| MD Anderson 2019a | No prediction model reported |
| MD Anderson 2019b | Predictors collected after starting treatment |
| Meade 2014 | No prediction model reported |
| Meyer 2007 | No prediction model reported |
| Meyer 2012a | No prediction model reported |
| Meyer 2012b | Conference abstract |
| Miah 2011 | Conference abstract |
| Miah 2013 | No prediction model reported |
| Miah 2016 | No prediction model reported |
| Mierzwa 2020 | Ineligible domain |
| Mierzwa 2021 | Predictors collected after starting treatment |
| Milano 2018 | No prediction model reported |
| Mohamed 2016 | Conference abstract |
| Mohamed 2019 | Conference abstract |
| Mohan 2022 | Predictors collected after starting treatment |
| Moiseenko 2012 | No prediction model reported |
| Monroe 2005 | No prediction model reported |
| Monroe 2014 | No prediction model reported |
| Monroe 2016 | No prediction model reported |
| Monti 2017 | No prediction model reported |
| Mori 2018 | Duplicate |
| Mori 2019a | Conference abstract |
| Mortensen 2011a | Conference abstract |
| Mortensen 2011b | Conference abstract |
| Mortensen 2013a | Radiotherapy technique outdated |
| Mortensen 2013b | No prediction model reported |
| Mosleh‐Shirazi 2019 | Ineligible domain |
| Mosleh‐Shirazi 2022 | No prediction model reported |
| Moubayed 2015 | Ineligible domain |
| Mouw 2010 | No prediction model reported |
| Munter 2004 | No prediction model reported |
| Munter 2007 | No prediction model reported |
| Musha 2015 | No prediction model reported |
| Musha 2016 | Conference abstract |
| Musha 2017a | Conference abstract |
| Musha 2017b | Conference abstract |
| Musha 2019 | No prediction model reported |
| Nakatsugawa 2015 | Conference abstract |
| Nakatsugawa 2016 | Conference abstract |
| Nakatsugawa 2019 | Methodological focus |
| Narayan 2008 | No prediction model reported |
| Narayanasamy 2014 | Conference abstract |
| Narayanasamy 2015 | No prediction model reported |
| Nardone 2018 | No prediction model reported |
| Nguyen 2019 | Ineligible domain |
| Niewald 1996 | No prediction model reported |
| Niyazi 2020 | Ineligible domain |
| O'Hare 2017 | No prediction model reported |
| Ogama 2010a | No prediction model reported |
| Ogama 2010b | No prediction model reported |
| Oh 2012 | Conference abstract |
| Oh 2019 | Conference abstract |
| Okamoto 2017 | No prediction model reported |
| Olteanu 2016 | Conference abstract |
| Orlandi 2019 | No prediction model reported |
| Ortholan 2009 | No prediction model reported |
| Otter 2012 | Conference abstract |
| Otter 2015 | No prediction model reported |
| Owosho 2017 | No prediction model reported |
| Ozkaya 2017 | No prediction model reported |
| Pagliara 2018 | No prediction model reported |
| Pagliara 2020 | Ineligible domain |
| Palazzi 2008 | No prediction model reported |
| Pardo‐Montero 2021 | Ineligible domain |
| Pareek 2019a | Conference abstract |
| Pareek 2019b | Conference abstract |
| Parsons 1994 | No prediction model reported |
| Patel 2016 | Conference abstract |
| Patterson 2011 | Ineligible outcome |
| Patterson 2018 | No prediction model reported |
| Peiffert 1997 | No prediction model reported |
| Periasamy 2016 | Conference abstract |
| Petersen 2011 | Conference abstract |
| Petersson 2021 | No prediction model reported |
| Petras 2019 | No prediction model reported |
| Pilz 2017 | Conference abstract |
| Pouliliou 2015 | No prediction model reported |
| Poulsen 2008 | Ineligible domain |
| Powrózek 2019 | No prediction model reported |
| Prameela 2016 | Not an original study |
| Pratesi 2011 | No prediction model reported |
| Prpic 2019 | No prediction model reported |
| Purkey 2009 | No prediction model reported |
| Pyakuryal 2014 | Conference abstract |
| Qin 2022 | Predictors collected after starting treatment |
| Quan 2016 | Conference abstract |
| Quik 2012 | Conference abstract |
| Rachman 2022 | Predictors collected after starting treatment |
| Radaideh 2016 | No prediction model reported |
| Rades 2017 | Ineligible domain |
| Radiation Oncology Summit | Conference abstract |
| Rancati 2021 | Conference abstract |
| Randall 2013 | No prediction model reported |
| Rao 2011a | Conference abstract |
| Rao 2011b | Conference abstract |
| Rao 2012 | Conference abstract |
| Reber 2023 | Methodological focus |
| Ricchetti 2016 | Conference abstract |
| Ritlumlert 2023a | Conference abstract |
| Robertson 2014 | Conference abstract |
| Robertson 2015a | Methodological focus |
| Robertson 2015b | Conference abstract |
| Robertson 2016 | Conference abstract |
| Rodrigues 2009 | No prediction model reported |
| Roenjom 2012a | Conference abstract |
| Roenjom 2012b | Conference abstract |
| Roenjom 2014 | Conference abstract |
| Roenjom 2016 | Conference abstract |
| Roesink 2005 | No prediction model reported |
| Rogers 2010 | No prediction model reported |
| Roick 2020 | No prediction model reported |
| Ruiz 2009 | Conference abstract |
| Rwigema 2017 | Conference abstract |
| Sachdev 2014a | Conference abstract |
| Sachdev 2014b | Conference abstract |
| Sachdev 2015 | No prediction model reported |
| Sakakura 2017 | Conference abstract |
| Salama 2008 | No prediction model reported |
| Saleh 2011 | Conference abstract |
| Saleh 2014 | Conference abstract |
| Samant 2023 | Methodological focus |
| Sanguineti 2007 | No prediction model reported |
| Sanguineti 2010 | Conference abstract |
| Sanguineti 2011 | Conference abstract |
| Sanguineti 2012 | Conference abstract |
| Sanguineti 2013a | No prediction model reported |
| Sanguineti 2013b | Conference abstract |
| Sanguineti 2014 | No prediction model reported |
| Sanguineti 2015 | No prediction model reported |
| Sanquineti 2010 | Conference abstract |
| Sapir 2016 | No prediction model reported |
| Sato 2014 | Conference abstract |
| Sayan 2017 | No prediction model reported |
| Schilstra 2001 | Methodological focus |
| Schuette 2019 | Conference abstract |
| Schultheiss 1990 | No prediction model reported |
| Schuurhuis 2018 | No prediction model reported |
| Schwartz 2010 | No prediction model reported |
| Self 2013 | No prediction model reported |
| Shelley 2019 | Conference abstract |
| Shi 2013 | No prediction model reported |
| Shi 2017 | Conference abstract |
| Shiao 2016 | Conference abstract |
| Shirasu 2018 | Conference abstract |
| Shukla 2017 | Conference abstract |
| Sijtsema 2016 | Conference abstract |
| Simon 1993 | No prediction model reported |
| Smith 2023 | Methodological focus |
| Smyczynska 2021 | Methodological focus |
| Soares 2016 | No prediction model reported |
| Somay 2023a | Ineligible domain |
| Sommat 2017 | No prediction model reported |
| Sommat 2018 | No prediction model reported |
| Sommat 2019 | No prediction model reported |
| Spiero 2023 | Methodological focus |
| Stathakis 2009 | No prediction model reported |
| Staton 2002 | No prediction model reported |
| Steenbakkers 2019a | Conference abstract |
| Steenbakkers 2019b | Conference abstract |
| Stewart 1982 | Not an original study |
| Stokkevåg 2019 | No prediction model reported |
| Straube 2016 | No prediction model reported |
| Strigari 2010 | No prediction model reported |
| Strigari 2012 | Radiotherapy technique outdated |
| Strigari 2016 | No prediction model reported |
| Studer 2006 | No prediction model reported |
| Su 2013 | No prediction model reported |
| Tagliaferri 2017 | Conference abstract |
| Tagliaferri 2017a | Ineligible domain |
| Tardini 2022 | Ineligible outcome |
| Tarmann 2017 | No prediction model reported |
| Taylor 1992 | Ineligible domain |
| Teguh 2008 | No prediction model reported |
| Teguh 2011 | Conference abstract |
| Teguh 2012 | Duplicate |
| Ten 2010 | Conference abstract |
| Teng 2019 | No prediction model reported |
| Tenhunen 2018 | Conference abstract |
| Thariat 2019 | No prediction model reported |
| Thukral 2023 | Methodological focus |
| Tol 2017 | Conference abstract |
| Topkan 2023 | No prediction model reported |
| Tsai 2010 | Conference abstract |
| Tsai 2014 | Conference abstract |
| Tuomikoski 2011 | Conference abstract |
| Turesson 1991 | No prediction model reported |
| Turnbull 2010 | Conference abstract |
| Urie 1992 | Ineligible domain |
| Usychkin 2011 | Conference abstract |
| Vai 2022 | No prediction model reported |
| Vainshtein 2014 | No prediction model reported |
| Valstar 2021 | No prediction model reported |
| Van den Bosch 2019 | Conference abstract |
| Van den Bosch 2021a | Conference abstract |
| Van den Broek 2006 | No prediction model reported |
| Van der Geer 2019 | No prediction model reported |
| Van der Laan 2012 | No prediction model reported |
| Van der Laan 2015 | No prediction model reported |
| Van der Schaaf 2012 | Conference abstract |
| Van Dijk 2013 | No prediction model reported |
| Van Dijk 2016 | No prediction model reported |
| Van Dijk 2017a | No prediction model reported |
| Van Dijk 2017b | Conference abstract |
| Van Dijk 2017c | Predictors collected after starting treatment |
| Van Dijk 2018a | No prediction model reported |
| Van Dijk 2018b | Duplicate |
| Van Dijk 2019 | Ineligible domain |
| Vangelov 2017 | No prediction model reported |
| Van Rijn‐Dekker 2021 | Conference abstract |
| Van Rossum 2020 | Ineligible domain |
| Vargo 2012 | Conference abstract |
| Vedelaar 2018 | Conference abstract |
| Venkatesh 2014 | No prediction model reported |
| Verheijen 2016 | Conference abstract |
| Vlacich 2012 | Conference abstract |
| Vlacich 2014 | No prediction model reported |
| Walker 2011 | No prediction model reported |
| Wang 2023a | Predictors collected after starting treatment |
| Wang 2023b | Predictors collected after starting treatment |
| Ward 2017 | Conference abstract |
| Werbrouck 2011 | Conference abstract |
| Wermker 2012 | Ineligible domain |
| White 2013 | No prediction model reported |
| Wilkie 2019 | Conference abstract |
| Willemsen 2019 | Conference abstract |
| Wojcieszynski 2019 | Conference abstract |
| Wopken 2012 | Conference abstract |
| Wu 2010 | No prediction model reported |
| Wu 2017 | Conference abstract |
| Wu 2018 | Predictors collected after starting treatment |
| Xavier 2015 | Conference abstract |
| Xiao 2016 | Conference abstract |
| Xie 2017 | Conference abstract |
| Xu 2010 | Conference abstract |
| Xu 2012a | Methodological focus |
| Xu 2012b | Methodological focus |
| Xu 2018 | No prediction model reported |
| Yahya 2016 | No prediction model reported |
| Yamazaki 2013 | No prediction model reported |
| Yamazaki 2015 | No prediction model reported |
| Yang 2016 | No prediction model reported |
| Yang 2017 | Ineligible outcome |
| Yao 2015 | No prediction model reported |
| Yao 2018 | No prediction model reported |
| Yasuda 2021 | Ineligible outcome |
| Yu 2015 | Conference abstract |
| Yuan 2014a | No prediction model reported |
| Yuan 2014b | No prediction model reported |
| Zauls 2013 | No prediction model reported |
| Zehetmayer 2000 | No prediction model reported |
| Zeng 2014 | No prediction model reported |
| Zhai 2017 | No prediction model reported |
| Zhang 2009 | No prediction model reported |
| Zhang 2015 | No prediction model reported |
| Zhang 2017 | No prediction model reported |
| Zhang 2020 | Ineligible domain |
| Zhou 2013 | Conference abstract |
| Zhou 2014a | No prediction model reported |
| Zhou 2014b | Conference abstract |
| Zhou 2017 | No prediction model reported |
| Zhou 2022 | Predictors collected after starting treatment |
| Zhu 2017 | No prediction model reported |
| Zou 2014 | No prediction model reported |
| Zuur 2009 | No prediction model reported |
Differences between protocol and review
Search methods
We originally planned to search for ongoing trials using the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) and Clinicaltrials.gov separately. Although it is also recommended to search both databases separately, we searched only the WHO ICTRP, which includes Clinicaltrials.gov. This was because: i) we already had many more studies than expected, and ii) it was not anticipated to find many eligible studies from the trial databases, given that this review focused on prediction modelling studies. We actually did not find any eligible studies from 217 trials retrieved from the WHO ICTRP.
Data collection/data extraction/risk of bias assessment
We originally planned that at least two review authors would independently perform the data extraction and risk of bias assessment. However, due to the unexpectedly large number of studies, one reviewer conducted these tasks, while another reviewer carefully verified the results for each study. For the studies retrieved in the first search in March 2021, four pairs of reviewers (TT and MT, EC and ES, ZD and JFN, MS and AT) handled the data extraction and risk of bias assessment. Similarly, for the studies retrieved in the updated search in January 2024, four pairs of reviewers (TT and MT, LMM and ZD, AT and DLI, ES and JC) were involved in the process.
Analysis
We planned to perform meta‐analyses of model performance if there were multiple external validations for identified NTCP models. However, we did not specify how many validations were sufficient for meta‐analyses. Based on discussion amongst co‐authors in collaboration with the Cochrane Prognosis Methods Group, we decided to perform meta‐analyses when there were five or more validations performed for the same model.
We planned to perform subgroup and sensitivity analyses. However, we were not able to perform them due to the small number of models externally validated.
GRADE
We did not use GRADE since, at the time of writing, there was no official GRADE guidance available for rating the certainty of the evidence for prediction models.
Contributions of authors
Toshihiko Takada: protocol development, screening and selection of studies, development of data extraction form and data extraction, characteristics of studies, risk of bias assessment, statistical analysis, writing and drafting of the review, communication with and between authors
Makbule Tambas: protocol development, screening and selection of studies, development of data extraction form and data extraction, characteristics of studies, risk of bias assessment, writing and drafting of the review, communication with and between authors
Enrico Clementel: screening and selection of studies, data extraction, characteristics of studies, risk of bias assessment, writing and drafting of the review
Artuur Leeuwenberg: screening and selection of studies, data extraction, characteristics of studies, risk of bias assessment, writing and drafting of the review
Marjan Sharabiani: screening and selection of studies, data extraction, risk of bias assessment
Johanna AAG Damen: protocol development, screening and selection of studies, development of data extraction form, methodological input on reviews of prognosis studies
Zoë S Dunias: screening and selection of studies, data extraction
Jan Nauta: screening and selection of studies, data extraction
Demy L Idema: screening and selection of studies, data extraction
Jungyeon Choi: screening and selection of studies, data extraction
Lotta M Meijerink: screening and selection of studies, data extraction
Johannes A Langendijk: medical and content input
Karel GM Moons: methodological input on reviews of prognosis studies
Ewoud Schuit: protocol development, screening and selection of studies, risk of bias assessment, writing and drafting of the review, communication with and between authors
Sources of support
Internal sources
-
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Netherlands
Julius Center for Health Sciences and Primary Care supported salary costs for Toshihiko Takada, Artuur Leeuwenberg, Johanna AAG Damen, Zoë S Dunias, Demy L Idema, Jungyeon Choi, Lotta M Meijerink, Karel GM Moons, and Ewoud Schuit.
-
Department of Radiation Oncology, University Medical Center, Groningen, Groningen University, Netherlands
University Medical Center, Groningen supported salary costs for Makbule Tambas, Jan Nauta, and Johannes A Langendijk.
-
European Organisation for Research and Treatment of Cancer, Belgium
European Organisation for Research and Treatment of Cancer supported salary costs for Enrico Clementel and Marjan Sharabiani.
External sources
-
European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 825162, Netherlands
Artuur Leeuwenberg's position was supported by European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 825162.
-
Belgian National Lottery, Belgium
Marjan Sharabiani's fellowship at the EORTC was supported by the Belgian National Lottery (Loterie Nationale/Nationale Loterij)
Declarations of interest
Toshihiko Takada: none known.
Makbule Tambas: none known.
Enrico Clementel: none known.
Artuur Leeuwenberg: none known.
Marjan Sharabiani: Funded by the European Organisation for Research and Treatment of Cancer.
Johanna AAG Damen: none known.
Zoë S Dunias: none known.
Jan Nauta: none known.
Demy L Idema: none known.
Jungyeon Choi: none known.
Lotta M Meijerink: none known.
Johannes A Langendijk: Member of the Global Advisory Board of IBA, for which honoraria was paid to the UMCG Research BV. Member of RayCare Clinical Advisory Board (no honorarium). The Department of Radiation Oncology of UMCG has research agreements with and funding from IBA, RaySearch and Elekta.
Karel GM Moons: none known.
Ewoud Schuit: none known.
Enrico Clementel and Marjan Sharabiani are authors of Sharabiani 2021. Johanes A Langendijk is an author of Beetz 2012a, Beetz 2012b, Beetz 2012c, Christianen 2012, Christianen 2016, Kalendralis 2022, Kamstra 2015, Kanayama 2018, Langendijk 2009, Langendijk 2021, Morimoto 2019, Rwigema 2019, Van den Bosch 2021, Van Rijn‐Dekker 2023, Wopken 2014a, Wopken 2014b. Karel GM Moons is an author of Van den Bosch 2021. Ewoud Schuit is an author of Langendijk 2021, Van den Bosch 2021, and Van Rijn‐Dekker 2023. These authors were not involved in evaluating those studies in which they had been involved (in the study conduct, analysis, and publication).
a.
These authors should be considered joint first author
Edited (no change to conclusions)
References
References to studies included in this review
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References to studies excluded from this review
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