Development and evaluation of a combined machine learning model for the prediction of acute GU toxicity in prostate cancer radiotherapy.

PURPOSE: To develop and internally evaluate a compact multimodal machine learning model for predicting acute genitourinary (GU) toxicity after prostate cancer radiotherapy and to assess the effect of validation design on estimated performance. METHODS: A retrospective cohort of 92 patients was analyzed, including 84 training patients and 8 hold-out test patients. Acute GU toxicity was modeled as a binary endpoint (RTOG Grade ≥ 1 versus Grade 0). From 495 clinical, DVH, radiomic, and dosiomic predictors, compact feature sets (k = 10 and k = 15) were derived using L1-based stability selection. Primary internal performance was estimated using repeated stratified nested cross-validation, with preprocessing, feature selection, and model fitting confined to each outer training fold. Results were compared with non-nested cross-validation using global feature selection before fold partitioning. RESULTS: Acute GU toxicity occurred in 33 patients, including 26 Grade 1 and 7 Grade 2 events. In nested cross-validation, the clinical-only baseline achieved the highest performance (ROC-AUC = 0.714 ± 0.108; PR-AUC = 0.626 ± 0.136), exceeding the compact k = 10 multimodal model (ROC-AUC = 0.611 ± 0.139; PR-AUC = 0.578 ± 0.159). For the same k = 10 model, non-nested cross-validation yielded higher estimates (ROC-AUC = 0.794 ± 0.116; PR-AUC = 0.793 ± 0.117), consistent with optimistic performance inflation from global feature preselection. CONCLUSIONS: Clinical variables remained the strongest predictors in this small cohort. Compact multimodal models were feasible and interpretable but did not improve leakage-free internal performance. These findings support nested cross-validation in small high-dimensional radiomics and dosiomics studies.