Background: Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape for metastatic urothelial carcinoma (mUC), yet reliable predictive biomarkers remain limited. The SamUR-AI study was designed to evaluate whether machine learning (ML) and explainable artificial intelligence (XAI) approaches could improve prediction of clinical outcomes in patients with mUC treated with ICIs. Materials and methods: We conducted a multicenter retrospective analysis including 438 patients treated with ICIs across 34 Italian institutions from the Meet-URO network. Baseline clinical and laboratory features were analyzed using ML and XAI methodologies to predict objective response rate (ORR), progression-free survival (PFS), and Results: Classification models showed suboptimal performance in predicting ORR (best test F1-score: 0.61), likely due to class imbalance and overfitting. In contrast, survival models achieved moderate predictive accuracy, with the extra survival trees model yielding a concordance index (C-index) of 0.67 for OS. SHapley Additive exPlanations-based line of immunotherapy and treatment combinations, liver and lung metastases, neutrophil count, and hemoglobin level. Conclusions: Although further validation is needed, our findings highlight the potential of XAI-enhanced ML to identify clinically relevant features and to support personalized treatment strategies in patients with mUC.
Predicting efficacy in patients with locally advanced/metastatic urothelial carcinoma (mUC) treated with immunotherapy using explainable machine learning approaches: the SamUR-AI trial on behalf of the Meet-URO group
Ferri, S.;Stellato, M.;Provenzano, L.;Pedrocchi, A. L. G.;Prelaj, A.;Miskovic, V.;
2026-01-01
Abstract
Background: Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape for metastatic urothelial carcinoma (mUC), yet reliable predictive biomarkers remain limited. The SamUR-AI study was designed to evaluate whether machine learning (ML) and explainable artificial intelligence (XAI) approaches could improve prediction of clinical outcomes in patients with mUC treated with ICIs. Materials and methods: We conducted a multicenter retrospective analysis including 438 patients treated with ICIs across 34 Italian institutions from the Meet-URO network. Baseline clinical and laboratory features were analyzed using ML and XAI methodologies to predict objective response rate (ORR), progression-free survival (PFS), and Results: Classification models showed suboptimal performance in predicting ORR (best test F1-score: 0.61), likely due to class imbalance and overfitting. In contrast, survival models achieved moderate predictive accuracy, with the extra survival trees model yielding a concordance index (C-index) of 0.67 for OS. SHapley Additive exPlanations-based line of immunotherapy and treatment combinations, liver and lung metastases, neutrophil count, and hemoglobin level. Conclusions: Although further validation is needed, our findings highlight the potential of XAI-enhanced ML to identify clinically relevant features and to support personalized treatment strategies in patients with mUC.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
