Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy

Urothelial carcinoma, predominantly appearing as non-muscle-invasive papillary urothelial carcinoma (NMIPUC), exhibits wide clinical variability. Accurate pathological staging and grading are essential for effective risk stratification and treatment decisions. Advancements in artificial intelligence (AI) open new opportunities to improve predictive models; however, their generalizability across diverse datasets remains to be addressed. This study developed a federated learning (FL)-based AI framework to enhance model robustness across institutions and predictive accuracy for non-muscle-invasive bladder cancer staging, grading, and a novel histological risk factor derived by clustering histological features for relapse prediction. Retrospective data, including 1437 NMIPUC cases from two institutions in Lithuania and Taiwan, were used for development and analysis. The FL models demonstrated improved robustness across participating institutions and higher accuracy compared to single-site models, achieving 86.2% accuracy for tumor stage and 79.2% for tumor grade, with minor performance variability across the datasets. Moreover, the novel histological risk factor outperformed conventional indicators of relapse-free survival (RFS) in NMIPUC patients treated with BCG immunotherapy, achieving hazard ratios of 2.7 (p = 0.0018) and 2.8 (p = 0.0208) in the Lithuania and Taiwan datasets, respectively. These findings highlight the potential of FL and histological feature-based AI models in providing robust, generalizable solutions for NMIPUC risk stratification and offer insights for personalized clinical interventions.