Expert opinion on biological therapy · 2026
Machine learning-integrated explainable artificial intelligence for survival prediction in urothelial carcinoma with enfortumab vedotin: an exploratory real-world analysis.
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Sridharan Kannan, Fiala Ondrej, Sivaramakrishnan Gowri, Matrana Marc R, Büttner Thomas, Kucharz Jakub, Molina Cerrillo Javier, Giannatempo Patrizia, Myint Zin W, Gómez de Liaño Alfonso, Pirshtuk Alina, Mota Augusto, Kanesvaran Ravindran, Ürün Yüksel, Angel Martin, Soule Tomas, Massari Francesco, Bellmunt Joaquim, Gupta Shilpa, Santoni Matteo
Аннотация
BACKGROUND: Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction.
METHODS: Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS). SHapley Additive exPlanations (SHAP) analysis (on best performing ML model) provided interpretability. Performance was assessed by C-index and time-dependent area-under-the-curve (AUC).
RESULTS: XGBoost (C-index 0.59) and Elastic Net (C-index 0.60) showed best discrimination. XGBoost achieved highest time-dependent AUCs (0.77, 0.87, 0.93 at 1, 2, 3 years). SHAP identified prior immunotherapy (pembrolizumab, atezolizumab/nivolumab), radiotherapy, and upper tract tumors with lower mortality risk; lung, liver, bone, soft tissue metastases increased risk. Eastern Cooperative Oncology Group performance status and metastatic distribution were key predictors.
CONCLUSION: ML with XAI identifies clinically plausible survival predictors in EV-treated aUC. XGBoost and Elastic Net offer modest risk stratification, that are hypothesis generating but does not support routine clinical use. Functional status, metastatic pattern, and treatment context are key drivers, providing a foundation for externally validated prognostic tools.
METHODS: Data from 544 aUC patients receiving EV after platinum chemotherapy and immunotherapy (51 centers, 24 countries) were analyzed. Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS). SHapley Additive exPlanations (SHAP) analysis (on best performing ML model) provided interpretability. Performance was assessed by C-index and time-dependent area-under-the-curve (AUC).
RESULTS: XGBoost (C-index 0.59) and Elastic Net (C-index 0.60) showed best discrimination. XGBoost achieved highest time-dependent AUCs (0.77, 0.87, 0.93 at 1, 2, 3 years). SHAP identified prior immunotherapy (pembrolizumab, atezolizumab/nivolumab), radiotherapy, and upper tract tumors with lower mortality risk; lung, liver, bone, soft tissue metastases increased risk. Eastern Cooperative Oncology Group performance status and metastatic distribution were key predictors.
CONCLUSION: ML with XAI identifies clinically plausible survival predictors in EV-treated aUC. XGBoost and Elastic Net offer modest risk stratification, that are hypothesis generating but does not support routine clinical use. Functional status, metastatic pattern, and treatment context are key drivers, providing a foundation for externally validated prognostic tools.