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.