Predicting Response to Intravesical Bacillus Calmette-Guérin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12-Center Cohort.

PURPOSE

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 (AI)-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.

MATERIALS AND METHODS

Pre-BCG resection-derived whole-slide images and clinical data were obtained for high-risk nonmuscle-invasive bladder cancer cases treated with BCG from 12 centers and were analyzed through a segmentation and feature extraction pipeline. Features associated with clinical outcomes were defined and tested on independent development and validation cohorts. Cases were classified into high or low risk for recurrence, progression, BCG-unresponsive disease, and cystectomy.

RESULTS

Nine hundred forty-four cases (development: 303, validation: 641, median follow-up: 36 months) representative of the intended use population were included (high-grade Ta: 34.1%, high-grade T1: 54.8%; carcinoma in situ only: 11.1%, any carcinoma in situ: 31.4%). In the validation cohort, "high recurrence risk" cases had inferior high-grade recurrence-free survival vs "low recurrence risk" cases (HR, 2.08, < .0001). "High progression risk" patients had poorer progression-free survival (HR, 3.87, < .001) and higher risk of cystectomy (HR, 3.35, < .001) than "low progression risk" patients. Cases harboring the BCG-unresponsive disease signature had a shorter time to development of BCG-unresponsive disease than cases without the signature (HR, 2.31, < .0001). AI assays provided predictive information beyond clinicopathologic factors.

CONCLUSIONS

We developed and validated AI-based histologic assays that identify high-risk nonmuscle-invasive bladder cancer cases at higher risk of recurrence, progression, BCG-unresponsive disease, and cystectomy, potentially aiding clinical decision making.