Abstract
Objectives
To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies.
Methods
The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).
Results
Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4–19), while all of included studies were assessed as having a high risk of bias (ROB) overall.
Conclusions
Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed.
Key Points
• Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer.
• The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins.
• The median Radiomics Quality Score of the included studies was 13 (range 4–19), and all of studies were considered to have a high risk of bias overall.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00330-022-09174-8.
Keywords: Carcinoma, non-small-cell lung; Solitary pulmonary nodule; Prognosis; Tomography, X-ray computed; Machine learning
Introduction
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for 85% of all cases [1, 2]. The precise survival risk stratification of patients with NSCLC is a crucial step in treatment. Although the tumor, node, and metastasis (TNM) classification for lung cancer is the most objective and authoritative indicator of the prognosis, those in identical tumor stages still have heterogeneous prognoses [3–5]. To improve the management of NSCLC and make proper treatment decisions, numerous studies have reported other independent clinical prognostic factors, including age, sex, and performance status [6–8]. In addition, medical imaging, such as CT, can also derive potential markers of prognosis, including tumor volume, pleura effusion, and radiomics [9–14].
Radiomics based on medical imaging can assess the tumor and its environment in its entirety, which can provide additional information for predicting cancer outcomes [15–17]. Several studies have successfully applied intratumor radiomics features to predict the overall survival, the prognosis of cancer recurrence, and time to progression in patients with NSCLC [17–19]. Other studies have investigated the clinical use of quantifying peritumoral regions at CT to help predict tumor invasiveness, tumor spread through air spaces, and especially prognostic outcomes [20–23]. For example, Wang et al found that the combination of radiomics features extracted from intra- and peritumoral areas could enhance the accurate prognosis prediction of pure-solid NSCLC [23]. However, the added value of extratumoral radiomics and the quality of the studies have not been systematically assessed to further explore the potential association between peritumoral radiomics features and prognosis in NSCLC.
Therefore, the aim of this study was to systematically review and appraise the results from published studies that examined the prognostic value of CT-based peritumoral radiomics features in NSCLC patients, and the potential biological underpinnings were also summarized.
Materials and methods
This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [24]. The review was registered on PROSPERO before initiation (registration no. CRD42022322916).
Search strategy
The PubMed, Embase, Web of Science, and Cochrane Library databases were comprehensively searched up to February 21, 2022, to identify studies that used CT-based peritumoral radiomics to evaluate the prognosis in patients with NSCLC. The reference lists of the included articles and the relevant literature were also manually searched. The following basic search terms were used: NSCLC, pulmonary nodule, CT, radiomics, peritumoral, and prognosis. The detailed search criteria are described in the supplementary material. The retrieval was performed without language and date restrictions.
Study selection
Original research articles will be included in the study. Eligibility criteria included the following: (1) patients with NSCLC; (2) evaluating the prognosis of patients by a peritumoral radiomics approach on CT. Studies were excluded if they (1) were case studies, editorials, letters, review articles and conference abstracts; (2) were not in the field of interest; or (3) were overlaps in study populations.
Data extraction
Data to be extracted will include the following: (1) study details: first author, publication year, country, study design; (2) patient details: the source of data acquisition (single-center/multicenter), type of cohort, sample size, TNM staging, histological subtype, type of treatment, prognostic outcome; (3) imaging details: CT tube voltage, reconstruction slice thickness (mm), plain or contrast CT; (4) radiomics details: segmentation software, segmentation method, peritumoral definition, and reference, feature extraction software, type of radiomics features, number of radiomics features, radiomics feature selection methods, type of models constructed, final classifier, number of radiomics features in the final model, type of radiomics features in the final model, and performance of the models. Two independent reviewers (L.W. and C.G.) completed the initial screening and extracted data from all enrolled studies.
Risk of bias assessment
The methodological quality of each study was evaluated by using the Radiomics Quality Score (RQS) [25] and the Prediction Model Risk of Bias Assessment Tool (PROBAST) [26]. The RQS provides a standardized and quantitative evaluation criterion for the methodology of radiomics researches. The RQS assessment contains sixteen key components from data selection, medical imaging, feature extraction, and exploratory analysis to modelling. Each item contributes to the final score and the total score ranges from -8 to 36 points [25]. Detail description of each item of RQS and the corresponding scores is provided in Table S1. PROBAST is a tool to assess the risk of bias (ROB) and the application of prediction models for diagnosis or prognosis. The risk of bias assessment of all enrolled studies was made by two reviewers (L.W. and C.G.) with a consensus agreement.
Results
Literature search and data extraction
The flow diagram of the literature search of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis is shown in Fig. 1. A total of 433 studies were identified, in which 432 studies were identified by the comprehensive literature search and one study was identified by a hand search of the relevant literature. After screening and evaluating, 13 studies with 2942 patients meeting the criteria were included in this systematic review [22, 23, 27–37].
Fig. 1.
Flowchart of the study screening and selection process of this systematic review
Patient and study characteristics
The patient characteristics of 13 studies are summarized in Table 1. The included studies were published from 2017 to 2022. Almost all the studies (12/13, 92%) were retrospectively designed [22, 23, 27–36], except one of the studies, which was prospective [37]. Patients from seven studies (7/13, 54%) were from one center [22, 28–30, 34, 36, 37], and the others (6/13, 46%) were from multiple center [23, 27, 31–33, 35]. Most studies (10/13, 77%) included a training cohort and validation/test cohort, in which six studies conducted external tests from another center [23, 27, 31–33, 35]. The number of patients included in the studies ranged from 90 to 592. The type of treatment and type of prognostic outcome are summarized in Table 1. The type of treatment varied, such as surgery, adjuvant chemo-/radiotherapy, and immune checkpoint treatment. The prognostic outcome included prediction of survival [23, 27–29, 31–35, 37], distant metastasis [22, 30, 36] and response status [28, 29, 31, 34]. The most frequent study purpose was the prediction of overall survival [27, 28, 29, 31,32, 34, 37] (7/13, 54%).
Table 1.
Basic characteristics and CT scanner information of the included studies
| Study ID | Country | Study design | Institution | Training cohort | Validation/test cohort | No. patient | Type of patients | Type of Treatment | Prognostic outcome | kVp | Reconstruction slice thickness (mm) | Contrast enhanced CT |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Tunali, 2017 [27] | USA | R | two | 61 | 47 (external) | 108 | Stage I–IV lung adenocarcinoma | Surgical resection | OS | 120 or 130 to140 | 1.5 or 2 or 2.5 or 3 | Yes |
| Dou, 2018 [22] | USA | R | one | 100 | 100 (internal) | 200 | Overall stage II-III lung adenocarcinoma | cCRT or trimodality (surgery + chemoradiation) or other except SBRT | Time to DM | 120 or 140 | 2.5 or 3.75 or 5 | Yes |
| Khorrami, 2019 [28] | USA | R | one | 45 | 45 (internal) | 90 | Stage III NSCLC | Neoadjuvant chemoradiation followed by surgical resection | OS; DFS; MPR | 110 to 140 | 0.6 to 5 | -- |
| Khorrami, 2019 [29] | USA | R | one | 53 | 72 (internal) | 125 | Stage IIIb or IV NSCLC | Front-line platinum-based chemotherapy | TTP; OS; RTC | -- | 1 to 5 | No |
| Antonoli, 2020 [30] | Italy | R | one | -- | -- | 124 | Stage IA to IIB NSCLC | Surgically or adjuvant chemo-/radiotherapy after surgery | TR (LR + DM); LR; DM | 120 | 2.5 | Yes |
| Khorrami, 2020 [31] | USA | R | two | 50 | 62 (internal); 27 (external) | 139 | Advanced NSCLC | PD1/PD-L1 inhibitor | OS; Rs | 100 to 120 | 1 to 5 | Yes |
| Pérez-Morales, 2020 [32] | USA | R | two* | 161 | 73 (internal); 62 (external) | 296 | Stage I-IV lung cancer; lung cancer by surgical resection | Surgical, Chemotherapy/Other, Radiation therapy | OS; PFS | -- | -- | -- |
| Vaidya, 2020 [33] | USA | R | Three# | 329 | 114 (external); 82 (external from TCIA) | 525 | Stage I, II resectable NSCLC | Surgery and/or adjuvant chemotherapy | DFS | -- | < = 5 | Yes/No^ |
| Vaidya, 2020 [34] | USA | R | one | 30 | 79 (internal) | 109 | Advanced NSCLC | Monotherapy with PD1/PD- L1 inhibitor | OS; HPs | -- | < = 5 | Yes/No^ |
| Liu, 2022 [35] | China | R | two | 142 | 62 (internal); 130 (external) | 334 | Clinical stage I solid lung adenocarcinoma | -- | DFS | 120 or 100 | 1 or 1.3 or 2 | Yes |
| Davey, 2021 [36] | UK | R | one | -- | -- | 203 | Stage I and IIA NSCLC | SBRT | DF | 120 | 3 | Yes |
| Amico, 2020 [37] | Italy | P | one | -- | -- | 97 | Stage III NSCLC | cCRT | OS | 140 | 3 | -- |
| Wang, 2022 [23] | China | R | Two& | 381 | 163 (internal); 48 (external from sdata) | 592 | Clinical stage and pathologic stage IA pure-solid NSCLC | Complete surgical resection | RFS | 120 | 1 | -- |
*: NLST + one center; #: two centers + TCIA database; &: one center + radiogenomics data set; ^: with either contrast-enhanced CT or non-contrast CT; --: not mentioned
cCRT, concurrent chemoradiotherapy; DF, distant failure; DFS, disease-free survival; DM, distant metastasis; HPs, identify hyperprogressors from other response patterns; IQR, interquartile range; LR, local recurrence; MPR, major pathological response; NLST, National Lung Screening Trial; NSCLC, non-small cell lung cancer; OS, overall survival; P, prospective; PFS, progression-free survival; R, retrospective; RFS, recurrence-free survival; Rs, discrimination of responders from nonresponders; RTC, response to chemotherapy; SBRT, stereotactic ablative radiotherapy; TTP, time to progression; TR, total recurrence; 95% CI, 95% confidence interval
Radiomics workflow
The details of the acquisition parameters of the images in the radiomics studies are shown in Table 1. The slice thickness of CT ranged from 0.6 to 5 mm in most studies except for one study in which the thickness was not mentioned [32]. Some studies (6/13, 46%) conducted radiomics using contrast-enhanced CT images [22, 27, 30, 31, 35, 36] while only one study used non-contrast CT images [29]. Two studies (2/13, 15%) conducted radiomics using either contrast-enhanced or non-contrast CT images for further analysis [33, 34]. The other studies (4/13, 31%) did not mention it [23, 28, 32, 37].
The study details of the radiomics workflow, including region of interest (ROI) segmentation, feature extraction and selection, and model construction, are summarized in Table 2. The ROI segmentation was manual in most studies (9/13, 69%) [22, 23, 28, 29, 31, 33–35, 37], semi-automatic in three studies [27, 30, 32], and automatic in one study [36]. The most commonly used software for ROI segmentation was 3D-Slicer (5/13, 38%), and the most commonly used software for radiomics feature extraction was MATLAB (8/13, 62%) in the included studies (Table 2). The types of extracted radiomics features included texture features and/or first-order statistics and shape features [22, 23, 28–37]. Moreover, some novel radiomics features were introduced in the studies. For example, Tunali et al generated radial gradient and radial deviation features that represent voxel-by-voxel gradient changes [27]. And Vaidya et al analyzed radiomics features of quantitative vessel tortuosity that represent the curvedness of tumor vessels [34].
Table 2.
Segmentation, extraction, selection methods, radiomics features, and prediction models of the included studies
| Study ID | Software (segmentation; features extraction) | Segmentation method | Type of Radiomics features | No. Radiomics features extracted | Radiomics features selection methods | Final radiomics model | Type of survival models constructed | No. Radiomics features in the final survival model | Type of radiomics features in the final survival model | The models with the best performance | The corresponding performance |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Tunali, 2017 [27] | Definiens Developer XD; MATLAB | Semi-automatic | Radial gradient; radial deviation | 48 | CCC; Pearson’s correlation analysis；Univariable analyses; Multivariable analyses | Multivariable Cox regression | 2 (radiomics model; clinical + radiomics model) | 2 | Radial deviation outside-border separation SD; radial gradient outside-tumor separation mean | -- | -- |
| Dou, 2018 [22] | Eclipse, Varian Medical Systems and SimpleITK toolbox; PyRadiomics (Python) | Manual | First-order statistics; shape; texture | 2175 | ICC；mRMR；Univariable analyses; Multivariable analyses | Multivariable Cox regression | 3 (clinical model; clinical + tumor radiomics model; clinical + rim radiomics model) | 2 | LoG 1.5mm 3D GLRLM RunEntropy; Wavelet LHL NGTDM complexity (tumor rim radiomics model) | Clinical + rim radiomics model (DM) | C-index 0.65 (test) (DM) |
| Khorrami, 2019 [28] | 3D-Slicer; MATLAB | Manual | Texture (Haralick, LBP; HOG; CoLlAGe; Law; Gabor); shape; first-order statistics | 1542 | ICC; mRMR; multivariable Cox regression | Multivariable Cox regression | 2 (radiomics model; clinical-pathological + radiomics model) | 4 (OS); 7 (DFS) | (Peritumoral Laws, intratumoral low frequency Gabor, tumor area, and intratumoral Law_Laplacian) (OS); (peritumoral Law_Laplacian, intratumoral Laws, intratumoral Haralick inertia, intratumoral Law_Laplacian, and intratumoral median frequency Gabor) (DFS) | Clinicopathologic + radiomics model (MPR); radiomics model (OS); radiomics model (DFS) | AUC 0.90 (training), 0.86 (test) (MPR); C-index 0.84 (training) (OS); C-index 0.78 (training) (DFS) |
| Khorrami, 2019 [29] | --; MATLAB | Manual | Laws energy measures; Laws Laplacian; Gabor; Haralick; LBP; HOG; CoLlAGe; 3D Shape | 1542 | ICC；mRMR；LASSO Cox regression model | LASSO Cox regression | 2 (radiomics model; clinical-pathologic + radiomics model) | 6(TTP); 4(OS) | (The intratumoral Haralick correlation, Haralick diff_entropy, and intratumoral Collage diff_entropy features) (OS); the intratumoral Laws features, the peritumoral Laws features (TTP) | Radiomics model (RTC); radiomics model (TTP); clinical-pathologic + radiomics model (OS) | AUC 0.82 (training), 0.77 (test) (RTC); C-index 0.86 (training) (TTP); C-index 0.77 (training) (OS) |
| Antonoli, 2020 [30] | Eclipse, Varian Medical Systems; Moddicom | Semi-automatic | Morphological; first-order statistics; texture; fractal-based | 94 | Mann-Whitney U test；“fscaret” R package；Cox proportional-hazard model | Multivariable Cox regression | 3 (radiomics model; clinicopathological model; clinicopathological + radiomics model) | 4(TR);5(LR); 5(DM) | szm.glnu.norm.peritomor; stat.90thpercentile.gtv; cm.inv.var.peritumor; morph.pca.flatness.peritumor (TR)；szm.glnu.norm.peritumor；rlm.rl.entr.gtv；zsm.z.perc.gtv；morph.av.gtv；stat.var.gtv(LR)；cm.inv.var.peritumor；rlm.srhge.gtv；least.gtv；rlm.lrhge.gtv；rlm.sre.gtv(DM) | Clinicopathological + radiomics model (LR; DM; TR) | AUC 0.750 (LR); AUC 0.759 (DM); AUC 0.760 (TR) |
| Khorrami, 2020 [31] | 3D-Slicer; MATLAB | Manual | Texture (Laws energy; Gabor; Haralick; CoLlAGe); first-order Statistics; shape | 618 | ICC; WLCX; LDA；unsupervised clustering analysis；univariable analysis; multivariable analysis | Multivariable Cox regression | 2 (DelRADx model; Clinicopathological + DelRADx model) | 8 | Haralick (per texture correlation increase); Haralick (per entropy increase); Laws (per heterogeneity increase) | DelRADx model (Rs); Clinicopathological + DelRADx model (OS) | AUC 0.88 (training), 0.85 (test 1), 0.81 (test 2) (Rs); C-index 0.72 (training), 0.69 (test 1), 0.68 (test 2) (OS) |
| Pérez-Morales, 2020 [32] | LuTA platform; toolboxes created in MATLAB and C+ + | Semi-automatic | -- | 264 | Univariable analysis; Pearson’s correlation analysis; multivariable analysis；CART | CART | 2 (radiomics model; clinical-pathological + radiomics model) | 2 | NGTDM Busyness (peritumoral); Statistical Root Mean Square (intratumoral) | Clinical-pathological + radiomics model (PFS; OS) | C-index 0.81 (training), 0.80 (test) (PFS); C-index 0.83 (training), 0.81 (test) (OS) |
| Vaidya, 2020 [33] | 3D-Slicer; MATLAB | Manual | Texture (Gabor; Haralick; Collage; Laws; Laplace); first-order statistics | 4464 | ICC; LASSO Cox regression model | LASSO Cox regression | 2 (radiomics model; clinical-pathological + radiomics model) | 13 |
CoLlAGe_peritumoral; Haralick_introtumoral |
Clinicopathologic + radiomics model (DFS) | C-index 0.74 (training)(DFS) |
| Vaidya, 2020 [34] | 3D-Slicer; MATLAB | Manual | Texture (Gabor; Haralick; Laws; Laplace; Collage); QVT | 4538 | ICC; unsupervised clustering analysis; supervised classifier (mRMR; RF/ LDA/ DLDA/ QDA/ SVM) | RF classifier* | 5 (radiomics model) | 3 |
Gabor feature family_5–10 mm peritumoral; QVT7_mean_peritumoral; QVT44_Entropy_Introtumoral |
Radiomics model (HPs) | AUC 0.85 (training) (HPs) |
| Liu, 2022 [35] | ITK-SNAP; IFoundry (GE Healthcare) | Manual | first-order statistics; shape; texture (GLCM; GLRLM); deep learning (VGG-19) | 5309 | Univariable Cox regression, LASSO Cox regression, correlation coefficient analysis, stepwise regression, multivariable Cox regression | Multivariable Cox regression | 11 (clinical model; pathological model; radiomics models; clinical-pathologic + radiomics model) | 10 (PTV-3~+3) |
Eight VGG19_deep learning based; first-order maximum; first-order interquartile range (PTV−3~+3) |
Clinicopathologic + PTV -3~ 3 radiomics model (DFS) | AUC 0.85 (training), 0.83 (test1), 0.83 (test 2) (DFS) |
| Davey, 2021 [36] | In-house, validated method; Pyradiomics (Python) | Automatic | First order statistics, texture (GLCM, GLSZM, GLRLM, NGTDM, GLDM) | 372 | Unsupervised (Spearman rank correlation analysis; Pearson correlation analysis); Supervised (univariable Cox regression/multivariable Cox regression/ mRMR) | Multivariable Cox regression | 13 (1 clinical model; 12 clinical + radiomics models) | 4 |
GLDM dependence variance_introtumor; ‘LDLGLE’ of GLDM_peritumor; ‘Small Dependence High GrayLevel Emphasis’ of GLDM_peritumor; GLCM MCC_peritumor |
Clinical + radiomics model (DF) | C-index 0.77 (DF) |
| Amico, 2020 [37] | --; MATLAB | Manual | First-order statistics; texture (3D GLCM; TOP-LBP) | -- | RFE:(AdaBoost; CART DT; RF; XGB); the single/total ranking strategy | AdaBoost; CART DT; RF; XGB | 24 (radiomics models) | 9 (AdaBoost_CTV) | U 3D LBP kurtosis; 3D LBP energy; RI 3D LBP maxAss; 3D LBP energy around maxAss; RI 3D LBP energy; U 3D LBP energy around maxRel; U 3D LBP entropy; U 3D LBP skewness; inverse GLCM (−1,−1,0) | AdaBoost_CTV radiomics model (OS) | AUC 0.83 (OS) |
| Wang, 2022 [23] | 3D-Slicer; PyRadiomics (Python) | Manual | Shape; first-order statistics; GLCM; GLSZM; GLRLM; NGTDM; GLDM; wavelet transform filter | 2163 | ICC; Correlation analysis; Univariable Cox regression analysis; variable clustering analysis; LASSO Cox regression | LASSO Cox regression | 3 (clinical model; radiomics model; clinical + radiomics model) | 18 | Eight intratumoral 3D ROI features, five intratumoral 2D ROI features, five peritumoral features (Sum Entropy; Correlation; Complexity; Gray Level Variance; Gray Level Nonuniformity Normalized) | Clinical + radiomics model (RFS) | C-index 0.81 (training), 0.77 (test 1), 0.75 (test 2) (RFS) |
AUC, the area under the receiver operating characteristic curve; C-index, concordance index; CART, Classification And Regression Tree; CART DT, CART Decision Tree; CoLlAGe, co-occurrence of local anisotropy gradient orientations; CTV, clinical target volume; DelRADx, radiomics base on the percent change of feature statistics between baseline and post- 6–8 week scans; DFS, disease-free survival; DLDA, diagonal linear discriminant analysis; DM, distant metastasis; GLCM, gray level co-occurrence matrix; GLDM, gray level dependence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; HOG, histogram of oriented gradient; HPs, distinguishing hyperprogressors from other responsers; ICC, intraclass correlation coefficient; LBP, local binary pattern; LDA, linear discriminant analysis; LR, local recurrence; MPR, major pathological response; NGTDM, neighboring gray tone difference matrix; OS, overall survival; PTV, peritumoral volume; QDA, quadratic discriminant analysis; QVT, quantitative vessel tortuosity; RF, random forest; RFE, recursive feature elimination; Rs, discrimination of responders from nonresponders; RTC, response to chemotherapy; SD, standard deviation; SVM, support vector machine; TR, total recurrence; TTP, time to progression; WLCX, Wilcoxon rank sum; XGB, XGBoost; --: not mention; *: the prognosis outcome is to identify hyperprogressors from other responsers
The number of extracted radiomics features ranged from 48 to 5309. The radiomics feature selection methods frequently included intraclass correlation coefficients, univariable analyses, and multivariable analyses. The types of models constructed in these studies ranged from 2 to 24. The model constructed in the final radiomics model was usually a multivariable Cox model [22, 27, 28, 30, 31, 35, 36]. The number of radiomics features in the final radiomics model ranged from 2 to 18.
The peritumoral radiomics model and possible biological underpinnings
All the included studies segmented both the intra- and peri-tumoral regions; however, the definitions of peritumoral regions varied. Three different definitions for peritumoral regions were summarized in Fig. 2. Almost all the performances of erosion and dilation were based on the morphology of tumors and can be classified into three types. In type 1, the border mask was defined to be inward erosion 12.5/15 mm [27] or 3 mm [22, 35, 36] to outward dilation 7.5/10 mm [27] or 3 mm [22, 35, 36] along the tumor border. The outside mask was defined as an area expanding outside from the tumor to 17.5/22.5 mm [27] or 3/6 mm [35]. The exterior mask was defined as an area 3 to 9 mm away from the tumor [22]. In type 2, the border mask was defined to be the region that expands 3 mm away from the tumor boundary [32] while the criteria for the outside mask was 15 mm [23, 28, 29, 33, 34] or 20 mm [30] or 30 mm [31]. In type 3, the gross tumor volume equalled the original volume of the tumor lesion without any erosion or dilation performance. The clinical target volume contained gross tumor volume plus an area expanding outside from tumor boundary. The planning target volume was defined as the combination of tumor volume and the area dilated from the tumor border, which was necessary to manage internal motion and set-up reproducibility [37].
Fig. 2.
The three different types of definitions for peritumoral regions were as follows: Type 1: Border Mask: (−12.5 or −15 to + 7.5 or +10) mm [27], (−3 to + 3) mm [22, 35, 36]; Outside Mask: (0 to +17.5 or + 22.5) mm [27], (0 to + 3 or + 6) mm [35]; Exterior Mask: (+3 to +9) mm [22]. Type 2: Border Mask: (0 to +3) mm [32]; Outside Mask: (0 to +15) mm [23, 28, 29, 33, 34], (0 to +20) mm [30], (0 to +30) mm [31]. Type 3: Tumor Mask: gross tumor volume; Border Mask: clinical target volume minus Tumor Mask; Outside Mask: planning target volume minus Tumor Mask [37]. −: inward erosion; +: outward dilation; 0: tumor boundary
Moreover, the references of peritumoral region researches applied also varied in these included studies. The peritumoral regions were often dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm (7/13, 54%) [23, 28–31, 33, 34]. Some of them (6/13, 46%) referred to previous findings, where a resection margin > 15 mm did not decrease the risk of recurrence or a resection margin ≥ 20 mm was the safe margin [28–31, 33, 34]. An area outside the border of the tumor was chosen as the peritumoral region in several studies (4/13, 31%), where microscopic extension of cancerous islets or “real invasive front” can still be found [22, 35–37].
Several researchers have explored the biological underpinnings of peritumoral radiomics features in the prediction of the prognostic outcome of patients with NSCLC [27, 31–33]. Khorrami et al investigated associations between changes in radiomics features and the density of tumor-infiltrating lymphocytes on digitized hematoxylin-eosin images [31]. Pérez-Morales et al analyzed the associations between the final two radiomics features with gene probesets [32]. Vaidya et al investigated associations between prognostic radiomics features and tumor-infiltrating lymphocytes (radiopathomic analysis), as well as the radiomics features and mRNA sequencing data (radiogenomic analysis) [33]. Tunali et al explored potential biological underpinnings by analyzing the correlations of radiomics features with semantic radiological features [27]. Others also discussed the possible pathological basis of prognostic radiomics features from the peritumoral region, such as “real invasive front,” hypoxic tumor environment, neovascularization and angiogenesis in the tumor microenvironment, lymphovascular tumor invasion and micrometastasis [22, 28–30, 34, 35].
The performance of the models
The models with the best performance and the corresponding performance metrics in the included studies were summarized in Table 2. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of these models in twelve of thirteen of included studies [22, 23, 28–37]. The peritumoral radiomics features played an important role in the survival models [22, 23, 27–37]. The values of C-index or AUC of these best-performance models ranged from 0.65 to 0.90 [22, 23, 28–37].
Quality assessment
The total RQS and the percentages of the maximum score are summarized in Table 3. The median RQS of the studies was 13 (range 4–19), and the corresponding percentage of the score was 36.11% (range 11.11–52.78%). Figure 3 shows the percentages of scores in the studies for the sixteen components of RQS. The results of the ROB and the applicability assessments of these studies were presented in Table 4. Figure 4 presents the percentage of the studies rated by level of concern, ROB, and applicability for each domain. All of studies were assessed as high ROB overall [22, 23,27–32, 33–37]. Most studies (12/13, 92%) were considered low concern regarding the applicability [22, 23, 28–33, 35–37].
Table 3.
Radiomics quality scores for the included studies
| Study ID | Image protocol quality (0–2) | Multiple segmentations (0 or 1) | Phantom study on all scanners (0 or 1) | Imaging at multiple time points (0 or 1) | Feature reduction or adjustment (−3 or 3) | Non radiomics features (0 or 1) | Biological correlates (0 or 1) | Cut-off analyses (0 or 1) | Discrimination statistics (0–2) | Calibration statistics (0–2) | Prospective study (0 or 7) | Validation (−5 to 5) | Comparison to gold standard (0 or 2) | Pontential clinical utility (0 or 2) | Cost-effectiveness analysis (0 or 1) | Open science and data (0–4) | Total points (−12 to 36) (%) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Tunali, 2017 [27] | 1 | 0 | 1 | 0 | 3 | 1 | 1 | 1 | 0 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 13 (36.11) |
| Dou, 2018 [22] | 1 | 1 | 1 | 0 | 3 | 1 | 1 | 1 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 2 | 17 (47.22) |
| Khorrami, 2019 [28] | 1 | 1 | 1 | 0 | 3 | 1 | 1 | 1 | 2 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 16 (44.44) |
| Khorrami, 2019 [29] | 0 | 1 | 1 | 0 | 3 | 1 | 1 | 1 | 2 | 1 | 0 | 2 | 0 | 2 | 0 | 0 | 15 (41.67) |
| Antonoli, 2020 [30] | 1 | 1 | 0 | 0 | 3 | 1 | 1 | 1 | 2 | 2 | 0 | -5 | 2 | 0 | 0 | 1 | 17 (47.22) |
| Khorrami, 2020 [31] | 1 | 0 | 1 | 0 | 3 | 1 | 1 | 1 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 16 (44.44) |
| Pérez-Morales, 2020 [32] | 0 | 1 | 1 | 0 | 3 | 1 | 1 | 1 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 14 (38.89) |
| Vaidya, 2020 [33] | 1 | 0 | 1 | 0 | 3 | 1 | 1 | 1 | 2 | 2 | 0 | 4 | 2 | 0 | 0 | 1 | 19 (52.78) |
| Vaidya, 2020 [34] | 1 | 1 | 1 | 0 | 3 | 0 | 1 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 12 (33.33) |
| Liu, 2022 [35] | 1 | 1 | 0 | 0 | 3 | 1 | 1 | 1 | 2 | 0 | 0 | 3 | 2 | 0 | 0 | 1 | 19 (52.78) |
| Davey, 2021 [36] | 1 | 0 | 0 | 1 | 3 | 1 | 1 | 0 | 2 | 0 | 0 | -5 | 0 | 0 | 0 | 1 | 7 (19.44) |
| Amico, 2020 [37] | 1 | 1 | 0 | 0 | 3 | 0 | 1 | 0 | 2 | 0 | 7 | -5 | 0 | 0 | 0 | 0 | 11 (30.56) |
| Wang, 2022 [23] | 1 | 1 | 0 | 0 | 3 | 1 | 1 | 1 | 2 | 1 | 0 | 3 | 2 | 2 | 0 | 1 | 19 (52.78) |
Fig. 3.
Quality assessment of included studies by the Radiomics Quality Score (RQS) and presenting the percentages of scores of the included studies
Table 4.
Prediction model risk of bias assessment of included studies (PROBAST)
| Study ID | Risk | Applicability | Overall | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Participants | Predictors | Outcome | Analysis | Participants | Predictors | Outcome | ROB | Applicability | |
| Tunali, 2017 [27] | - | + | - | - | + | - | ? | - | - |
| Dou, 2018 [22] | - | ? | ? | - | + | + | + | - | + |
| Khorrami, 2019 [28] | - | + | ? | - | + | + | + | - | + |
| Khorrami, 2019 [29] | - | + | + | - | + | + | + | - | + |
| Antonoli, 2020 [30] | - | ? | ? | + | + | + | + | - | + |
| Khorrami, 2020 [31] | - | ? | ? | - | + | + | + | - | + |
| Pérez-Morales, 2020 [32] | - | ? | ? | - | + | + | + | - | + |
| Vaidya, 2020 [33] | - | ? | ? | + | + | + | + | - | + |
| Vaidya, 2020 [34] | - | ? | ? | - | + | + | ? | - | ? |
| Liu, 2022 [35] | - | ? | ? | - | + | + | + | - | + |
| Davey, 2021 [36] | - | ? | ? | - | + | + | + | - | + |
| Amico, 2020 [37] | + | + | ? | - | + | + | + | - | + |
| Wang, 2022 [23] | - | ? | ? | + | + | + | + | - | + |
ROB, risk of bias; PROBAST, prediction model risk of bias assessment tool; + indicates low ROB/low concern regarding applicability; − indicates high ROB; ? indicates unclear ROB/unclear concern regarding applicability
Fig. 4.
The percentage of the included studies rated by the risk of bias and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST)
Discussion
In this systematic review, we found that the radiomics features extracted from the peritumoral lung parenchyma on CT images can be considered a potential prognostic factor for patients with NSCLC. However, the included studies showed considerable variability and heterogeneity (including CT acquisition parameters and radiomics methodology) in each step of radiomics analysis.
Using standardized radiomics analysis was advocated to eliminate unnecessary confounding variability [25, 38]. With included studies having a wide range of section thicknesses (0.6–5 mm), the impact of section thickness on the performance of the model should be evaluated. Khorrami et al evaluated the impact of section thickness on the performance of the classifier and found that the areas under the receiver operating characteristic curves for the radiomics model decreased slightly when the section thickness increased [28, 29, 31]. Bettinelli et al found that the agreement of seven radiomics software programs varied [39]. The test-retest and differences in the inter-CT and intra-CT protocols can affect the stability of radiomics features to different degrees [40]. Therefore, several studies selected stable and reproducible features on the test-retest RIDER lung CT dataset and retained features with an intraclass correlation coefficient of 0.75, 0.8, 0.85 or greater [22, 27–29, 31–34].
ROIs can be segmented manually or (semi)automatically. However, manual segmentation remained the main method in the radiomics studies, and 69% of included studies segmented the ROI manually [22, 23, 28, 29, 31, 33–35, 37]. The variability in manual delineations can be reduced by multiple segmentation, but it is time-consuming [25]. Hence, rapid and reliable automatic ROI segmentation is highly desired and is still challenging. Some efforts to automatically segment the lung nodules have been made, which is promising in the future [41–43]. Feature selection, modeling methodology, and validation were three major aspects of the radiomics model. Feature reduction for high-throughput radiomics features was performed to decrease the risk of overfitting by multiple methodologies, such as max-relevance and min-redundant, the least absolute shrinkage and selection operator method [22, 28, 29, 33, 35]. Validation is an indispensable component of radiomics analysis [25]. Most of the included studies conducted internal validation or even external validation from another center [22, 23, 27–29, 31–35].
CT images may contain information that reflects the underlying pathophysiology of the tumor and that results in the conversion of images into structured data to assist in clinical decision support [38]. Peritumoral mask segmentation is usually based on morphologic operations (dilation) from the lesion boundary. Features are often extracted from three-dimension volume of interest and/or a section-by-section basis [22, 23, 27–31], while a few studies extracted from the three slices have the maximum area of the tumor [33, 34]. With an underlying biological rationale, such as “real invasive front” and micrometastasis around the tumor, the peritumoral regions of the included studies were dilated from the tumor boundary between 3 and 30 mm [22, 23, 27–36]. The biological underpinning of radiomics is significantly important to its wider use and further validation. Efforts to explain the biological meaning of radiomics are emerging, including relationships with semantic features, gene expression, microscopic histopathologic findings, and macroscopic histopathologic marker expression [44]. Encouragingly, several researchers have investigated the correlation between prognostic radiomics features and the density of tumor-infiltrating lymphocytes and gene and mRNA sequencing data [31–33]. This exploration will reinforce our understanding of the biological meaning of peri-tumoral radiomics in the predicting prognosis of NSCLC patients.
The RQS was used to assess the methodology, analysis, and reporting of a radiomics study. The median RQS of the studies was 13 (range 4–19), which indicates that most of the included studies did not reach a median level of radiomics quality. All the included studies conducted feature reduction, and biological correlates discussions. None of the included studies conducted a cost-effectiveness analysis, and most of the studies lacked open science. According to the PROBAST, all of the studies were considered to have a high ROB overall. The reasons for model development and validation studies with high ROB may be as follows: (1) Most of the included studies (12/13, 92%) were retrospective studies. (2) The calibration was not evaluated in most studies. (3) Whether predictors were assessed without knowledge of outcome information was also not mentioned.
This systematic review has several limitations that should be noted. First, the number of eligible studies was relatively small. Second, because high heterogeneity was found in radiomics analysis, such as the type of treatment, outcome of prognosis, and radiomics modeling, a meta-analysis of pooled outcomes was not conducted. Third, most of the studies were evaluated as having low RQS and high ROB, so the results should be interpreted with caution.
In conclusion, growing evidence has shown that peritumoral CT-based radiomics features in predicting the prognosis of NSCLC are promising, although they need standardization in radiomics analysis. Because most of the studies were performed retrospectively, studies based on prospective, multiple centers as well as biological correlations should be further conducted to promote their clinical use.
Supplementary Information
(DOCX 32 kb)
Abbreviations
- AUC
Area under the receiver operating characteristic curve
- C-index
Concordance index
- NSCLC
Non-small cell lung cancer
- PROBAST
Prediction model risk of bias assessment tool
- ROB
Risk of bias
- ROI
Region of interest
- RQS
Radiomics quality score
- TNM
Tumor, node, and metastasis
Funding
The study was supported by the National Natural Science Foundation of China [Grant No.82102128], the Medical Health Science and Technology Project of Zhejiang Province (Grant No. 2022KY230), and “Pioneer” and “Leading Goose” R&D Program of Zhejiang (Grant No. 2022C03046).
Declarations
Guarantor
The scientific guarantor of this publication is Chen Gao.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Informed consent
Written informed consent was not required for this study because this was a systematic review.
Ethical approval
Institutional Review Board approval was not required because this was a systematic review.
Methodology
• retrospective
• Systematic review
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Maosheng Xu, Email: xums166@zcmu.edu.cn.
Chen Gao, Email: doctor_gaochen@zcmu.edu.cn.
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