Interpretable machine learning for individualized survival prediction in node-positive, non-metastatic prostate cancer: a population-based study.

BACKGROUND: Prostate cancer with regional lymph node involvement but no distant metastasis (N1M0) has heterogeneous prognosis. This study aimed to develop and validate an interpretable machine learning model for predicting cancer-specific survival (CSS). METHODS: Data from 18,287 N1M0 patients (2000-2022) in the SEER database were divided into training (n = 4780), internal testing (n = 1193), and two temporal validation cohorts (n = 3149; n = 9165). Cox proportional hazards and four machine learning models were compared using C-index, time-dependent AUC, and Integrated Brier Score. SHAP was used for interpretability, and IPTW for sensitivity analysis. RESULTS: Random Survival Forest (RSF) outperformed all models, achieving the highest C-index (0.697 internal; 0.740 temporal validation). RSF-stratified risk groups showed significant survival differences (P < 0.001). SHAP revealed radical prostatectomy as the strongest protective factor, followed by lower T stage and PSA. IPTW confirmed survival benefits of aggressive local control. CSS outperformed overall survival in discriminative accuracy. CONCLUSION: The RSF model provides accurate, robust, and interpretable prognostication for N1M0 prostate cancer. Deployed as a web-based calculator, it enables precise risk stratification and individualized treatment planning.