ArticleBMC cancer2026
Development and internal validation of an explainable machine-learning model to predict 3-year overall survival rate after radical cystectomy.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis 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.
methodsWe 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.
resultsIn 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.
conclusionsWe 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 registrationThis study did not involve a prospective clinical trial.
trial registrationNot applicable.
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