Development and validation of artificial intelligence-based model for bladder cancer immunophenotyping using whole slide images.

The classification of immunophenotypes in muscle-invasive bladder cancer (MIBC) is critical for predicting immunotherapy response and clinical outcomes, yet current assessment methods lack standardization and scalability. We developed and validated an artificial intelligence-based MIBC Immunophenotype Diagnostic System using computational pathology to enable reproducible classification from routine hematoxylin and eosin-stained whole-slide images. In this multicenter retrospective diagnostic study, consecutive patients who underwent partial or radical cystectomy between 2014 and 2024 from two Chinese hospitals and The Cancer Genome Atlas cohort were included, with an independent cohort receiving immune checkpoint inhibitors for treatment efficacy evaluation. The system integrates Hover-Net-based nuclear classification with cell structure graph networks to model spatial cellular interactions within the tumor microenvironment. Across external validation cohorts, the model achieved macro-area under the curve values of 0.922-0.956 and macro-accuracy of 0.922-0.950, demonstrating robust generalizability. In a human-AI collaboration study, the system outperformed junior and senior pathologists and significantly improved junior pathologists' diagnostic accuracy while reducing review time. Predicted Inflamed tumors exhibited enriched CD8+ T-cell infiltration, elevated checkpoint gene expression, and stronger correlation with immunotherapy response. These findings support clinical translation for precision immuno-oncology in bladder cancer.