Table 2.
Clinical scenarios and necessity of using dynamic prediction approaches with representative cases
| Variables | N (%) | Representative cases |
|---|---|---|
| Disease area | ||
| Breast cancer | 29 (16.7) | 16/29 analyzed the impact of intervening events (e.g., recurrence) on prognosis; 11/29 constructed dynamic prognostic models, among which one employed longitudinal NLP to process unstructured medical reports as multi-nomics data. |
| Prostate cancer | 22 (12.6) | 18/22 explored the impact of dynamic PSA on prognosis in various types of prostate cancer; 14/22 constructed dynamic prognostic models; no study considered intervening events. |
| Lung cancer | 21 (12.1) | 6/21 analyzed the impact of post-treatment cfDNA monitoring (e.g., negative and positive) on prognosis; 6/21 explored the effect of changing tumor size on prognosis using different methods. |
| Pan-cancer | 17 (9.8) | 6/17 explored the impact of changing tumor size on prognosis using various methods; 4/17 utilized scale scores as dynamic predictive values; 10/17 constructed dynamic prognostic models. |
| Colorectal cancer | 15 (8.6) | One combined longitudinal MRI images to establish a multitask DL model to predict treatment response; One used longitudinal CT images to predict the ability of early treatment response with DL; other longitudinal indicators were comparatively diverse. |
| Liver cancer | 12 (6.9) | One used time-series clinical variables to dynamically prognosticating patients’ survival paths. |
| Blood cancer | 11 (6.3) | One analyzed the correlation between dynamic MRD and PFS. |
| Bone and soft tissue cancer | 8 (4.6) | 5/8 utilized landmark Cox models to assess the correlation between intervening events (e.g., LRR, DM) and OS. |
| Head and neck cancer | 8 (4.6) | One constructed a prognostic model based on longitudinal cfEBV DNA load post-treatment, employing joint model to make risk stratification, determining follow-up schedules, and selecting candidates for adjuvant therapy. |
| Pancreatic cancer | 5 (2.9) | One assessed whether longitudinal monitoring of SIII had prognostic value for OS. |
| Melanoma | 4 (2.3) | One analyzed the dynamic changes of circulating soluble PD-1 and PD-L1 and their association with OS. |
| Ovarian cancer | 4 (2.3) | All used joint model finding longitudinal variable CA125 was associated with prognosis in patients with different types of ovarian cancer. |
| Urothelial cancer | 4 (2.3) | All focused on the correlation between longitudinal changes in tumor size and prognosis. |
| Brain cancer | 3 (1.7) | 2/3 included tumor volume measurement as a longitudinal variable, discovering its value and temporal patterns in predicting prognosis. |
| Gastric cancer | 3 (1.7) | 2/3 modeled recurrence as an intervening event in multi-state model, which could help identify effective factors affecting death. |
| Childhood cancer | 2 (1.1) | One used a 10-year childhood cancer survivors’ cohort to identify three trajectory groups to represent temporal diagnostic pattern and compared the late mortality among these groups. |
| Esophageal cancer | 2 (1.1) | One analyzed longitudinal health-related quality of life and time-to-event endpoint using joint models assuming different (e.g., linear or spline-based) trajectories. |
| Renal cancer | 2 (1.1) | One developed a dynamic prognostic model for kidney renal clear cell carcinoma patients by combining clinical and genetic scores generated by prediction models. |
| Pleural mesothelioma | 1 (0.6) | The study treated linear thickness, disease volume, and normalized lung volume as time-dependent variables and evaluated their associations with overall survival. |
| Thyroid cancer | 1 (0.6) | The study developed a model to estimate the effects of long-term dosing with motesanib and survival, incorporating tumor size at different time points. No dynamic study was found after 2010. |
| Study purposes (Multiple choices) | ||
| To evaluate association and/or prognostic value | 122 (70.1) | The earliest study purpose. In 1991, one used an intermediate event (i.e., ipsilateral breast tumor recurrence) as a time-dependent covariate to determine whether it was an important predictor of DDFS in patients with breast cancer after lumpectomy. |
| To construct prognostic prediction model | 80 (46.0) | Various dynamic prediction approaches could be utilized. As early as 2005, one constructed a joint model to incorporate post-treatment follow-up PSA and clinical recurrence and to make individualized prediction. |
| To study disease transition | 17 (9.8) | 16/17 used multi-state model and 1/17 used joint model. As early as 1999, one investigated transitions of breast cancer disease, including the first tumor, LRR, DM, or death using a multi-state model, which allowed to study the effect of covariates for each transition, considering patients’ history. |
| To select significant factors | 13 (7.5) | One evaluated the prognostic impact of the initial status and trajectories of longitudinal muscle and BMI values as a prognostic factor on OS in patients with CRC using both Cox model with summary statistics and two-stage Cox model. |
| To determine surrogate endpoints or biomarkers | 13 (7.5) | One assessed time to nadir and depth of nadir as surrogates for OS in metastatic CRC using joint models. |
| To provide personalized treatment strategies | 6 (3.4) | One developed a model for predicting the risk of Gleason upgrading in patients with prostate cancer on active surveillance and using the predicted risks to create risk-based personalized biopsy schedules as an alternative to one-size-fits-all schedules. |
| Necessity of using dynamic prediction approaches | ||
| A1-Making better prediction | 77 (44.2) | All AI studies incorporating dynamic predictors were to make better prediction. One developed an AI framework integrating longitudinal electronic health records with real-world data to enable continuous pan-cancer prognostication. |
| A2-Investigating correlation | 73 (42.0) | Various methods could be used to investigate the relationship between dynamic predictors and prognosis. Recently, one evaluated predictive value of the dynamic ctDNA during chemoradiotherapy with clinical outcomes for locally advanced NSCLC patients. |
| A3-Including intervening events | 16 (9.2) | One employed a semi-Markov multi-state model for the simultaneous analysis of various endpoints (i.e., LRR, DM) describing the course of breast cancer. |
| A4-Identifying risk factors | 8 (4.6) | One assessed the trajectories of CEA, CA19-9, and CA125 within 3 years after surgery, and evaluated the impact of these three tumor markers jointly on CRC outcomes in terms of preoperative levels and longitudinal trajectories. |
NLP natural language processing, PSA prostate-specific antigen, cfDNA cell-free DNA, ctDNA circulating tumor DNA, cfEBV DNA circulating cell-free Epstein-Barr virus DNA, MRD minimum residual disease, PFS progression-free survival, OS overall survival, LRR local regional recurrence, DM distant metastasis, DDFS distant-disease-free survival, SLD sum of the longest diameters, NLR neutrophil-to-lymphocyte ratio, NSCLC Non-Small Cell Lung Cancer, BMI body mass index, AI artificial intelligence, CEA Carcinoembryonic antigen, CA19-9 carbohydrate antigen 19-9, CA125 carbohydrate antigen 125, CRC colorectal cancer.