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Impact of Computational Histology AI Biomarkers on Clinical Management Decisions in Non-Muscle Invasive Bladder
Vignesh T Packiam1, Saum Ghodoussipour1, Badrinath R Konety2
1Rutgers Cancer Institute, New Brunswick, NJ 08901, USA.
Cancers
|January 28, 2026
Summary
Computational Histology Artificial Intelligence (CHAI) tests significantly altered non-muscle invasive bladder cancer (NMIBC) management in 67% of cases. These AI-driven insights guided treatment decisions, improving precision care and addressing Bacillus Calmette-Guérin (BCG) shortages.
Area of Science:
- Urology
- Oncology
- Artificial Intelligence in Medicine
- Computational Pathology
Background:
- Non-muscle invasive bladder cancer (NMIBC) management faces challenges from conflicting guidelines, Bacillus Calmette-Guérin (BCG) shortages, and the need for alternative therapies.
- Computational Histology Artificial Intelligence (CHAI) tests offer insights into tumor specimens, predicting BCG response and recurrence/progression risks for precision medicine.
- CHAI tests provide practical advantages, including rapid results and no tissue consumption, addressing unmet clinical needs.
Purpose of the Study:
- To assess the impact of CHAI tests on physician decision-making in routine, real-world NMIBC management.
- To evaluate how CHAI biomarker results influence treatment plans and clinical outcomes.
- To explore the utility of CHAI in optimizing NMIBC care, particularly amid BCG shortages.
Main Methods:
- Physicians at six centers utilized CHAI tests (Vesta Bladder) during routine NMIBC care.
- Tumor specimens were analyzed using H&E-stained slides and CHAI assay to extract histomorphic features.
- Ordering physicians were surveyed to assess pre- and post-test management plans and result usefulness for 105 high-grade NMIBC cases.
Main Results:
- CHAI test results influenced primary management in 67% (70/105) of cases.
- Management changes included modality shifts (cystectomy or bladder-sparing) and intravesical agent adjustments.
- Therapeutic agent selection was significantly guided by predictive biomarkers for BCG response (80% change vs. 48%).
Conclusions:
- CHAI biomarker results demonstrably influenced clinical decision-making in two-thirds of routine NMIBC cases.
- BCG predictive biomarkers effectively guided intravesical agent selection, crucial during shortages.
- Prognostic risk stratification informed treatment escalation/de-escalation, supporting precision NMIBC management.
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