Hybrid Machine Learning Models Integrating VI-RADS and Clinical Metrics for Bladder Cancer Staging

Objective In bladder cancer, integrating imaging and non-imaging parameters may enhance diagnostic performance beyond the Vesical Imaging-Reporting and Data System (VI-RADS). This study aimed to develop and validate machine learning models incorporating VI-RADS scores with clinical and laboratory variables to predict muscle invasion and support individualized treatment decisions. Materials and Methods A total of 372 patients who underwent transurethral resection of bladder tumor between 2019 and 2024 and had preoperative mpMRI performed according to the VI-RADS protocol were retrospectively evaluated. VI-RADS scores were combined with demographic data, hematological indices, biochemical markers, and urinalysis findings to construct predictive models. Machine learning algorithms—including logistic regression, random forest, support vector machines, extreme gradient boosting, light gradient boosting machine, and deep neural networks—were developed and optimized. Model performance was assessed using receiver operating characteristic area under the curve (AUC), sensitivity, specificity, Brier score, and decision curve analysis (DCA) and compared with VI-RADS alone. Results Pathological muscle invasion (≥ T2) was identified in 103 (27.8%) of the 372 patients. VI-RADS alone yielded an AUC of 0.89. Models supported with clinical and laboratory parameters demonstrated significant improvement, particularly random forest (AUC = 0.95), support vector machines (AUC = 0.95), and logistic regression (AUC = 0.94). Calibration analysis of the isotonic regression–adjusted random forest model yielded a slope of 1.16 and an intercept of − 0.039, indicating probability estimates closely aligned with clinical reality. In DCA, the RF model outperformed both the “treat-all” and “treat-none” strategies, demonstrating clear net clinical benefit. Conclusion Integrating VI-RADS with clinical and laboratory parameters improves discrimination and calibration in predicting muscle invasion compared with imaging alone. The random forest model, in particular, may reduce misclassification at critical decision points—such as early radical cystectomy or neoadjuvant chemotherapy—and provide more reliable information for patient counseling.