Comprehensive Summary
This publication by Chang et al focuses on proposing solutions to promote consensus between those working to stratify risk in regard to high-grade (HG) Ta non–muscle-invasive bladder cancer (NMIBC). Utilizing Computational Histology Artificial Intelligence, or CHAI, biomarkers indicative of recurrence showed superior performance to current clinical risk models following treatment with adjuvant intravesical bacillus Calmette-Guérin treatment with transurethral resection of bladder tumor. This was a multi-center study focusing on expanding information on CHAI as previously validated in high-risk NMIBC cohort.
Outcomes and Implications
Clinical significance of this study is indicated through the improved stratification as evidenced by the enhanced categorization compared to current AUA and EAU guidelines. This can greatly improve monitoring capabilities as patients normally classified as moderate risk had high-risk qualities when employing CHAI. Reclassifying patients based on this novel assay may improve clinical decision-making and subsequent patient management.