Predicting response to intravesical BCG in high-risk non-muscle invasive bladder cancer using an artificial intelligence-powered pathology assay: development and validation in an international 12 center cohort

There are few markers to identify those likely to recur or progress after treatment with intravesical bacillus Calmette-Guérin (BCG). We developed and validated artificial intelligence-based histologic assays that extract interpretable features from transurethral resection of bladder tumor digitized pathology images to predict risk of recurrence, progression, development of BCG-unresponsive disease, and cystectomy.