Machine learning prediction of postoperative recurrence in bladder cancer using clinical and laboratory indicators.

Postoperative recurrence is a major determinant of prognosis in bladder cancer. Early identification of patients at high risk is essential for optimizing individualized follow-up and therapeutic strategies. This study aimed to develop a comprehensive recurrence risk prediction model based on clinical characteristics, laboratory parameters, and postoperative follow-up data, and to identify the key risk factors associated with recurrence. A total of 488 patients with bladder cancer were retrospectively enrolled. Demographic, lifestyle, comorbidity, tumor-related, surgical, and laboratory data at 3 months postoperatively were collected. Univariate and multivariate analyses were conducted to identify recurrence-associated variables. Predictive models were constructed using four machine learning algorithms: eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (KNN). Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and k-fold cross-validation. Feature importance and individual risk contributions were interpreted using SHAP (SHapley Additive exPlanations) analysis. Age, smoking history, tumor stage, tumor number, tumor size, pathological grade, neutrophil-to-lymphocyte ratio (NLR), urine cytology, hematuria, NMP22, and alkaline phosphatase (ALP) were identified as independent predictors of bladder cancer recurrence. Among all models, XGBoost demonstrated the best predictive performance, with an AUC of 0.960 in the training set, 0.925 in the validation set, and 0.850 in the external validation cohort. SHAP analysis revealed that smoking history, tumor stage, tumor number, tumor size, pathological grade, NLR, urine cytology, hematuria, and NMP22 were the most influential predictors of recurrence and contributed significantly to inter-individual risk differences. The multidimensional machine learning–based recurrence prediction model developed in this study accurately identifies high-risk bladder cancer patients and elucidates key risk factors, offering a robust evidence base for personalized postoperative surveillance and intervention. Furthermore, it provides novel insights into the biological mechanisms underlying recurrence. Future studies with larger, multicenter cohorts are warranted to validate the model’s robustness and clinical applicability.