Development and internal validation of an explainable machine-learning model to predict 3-year overall survival rate after radical cystectomy

BACKGROUND: This study aimed to develop and internally validate an explainable machine-learning model using routinely available clinicopathologic and laboratory variables for predicting 3-year overall survival (OS) after radical cystectomy. METHODS: We retrospectively included 300 patients who underwent radical cystectomy between January 2018 and December 2022. The primary endpoint was prespecified as death within 3 years after surgery, chosen as a clinically relevant fixed-time milestone with relatively complete follow-up at this horizon in our cohort. Predictors were selected in the training set using LASSO logistic regression followed by random-forest recursive feature elimination. RESULTS: In internal validation, AUCs ranged from 0.834 to 0.950. CatBoost achieved the best overall classification performance (AUC = 0.931, accuracy = 0.862, sensitivity = 0.647, specificity = 0.951, PPV = 0.846, and NPV = 0.867). SHAP analyses identified tumor stage (T, N, and M stage) as the dominant drivers of predicted risk, with additional contributions from age, BMI, albumin, globulin, lymphocyte count, platelet count, and preoperative creatinine. CONCLUSIONS: We developed an internally validated, SHAP-interpretable CatBoost model for predicting 3-year overall survival (OS) after radical cystectomy. External validation and recalibration in independent cohorts are required before clinical use. TRIAL REGISTRATION: This study did not involve a prospective clinical trial. TRIAL REGISTRATION: Not applicable.