Impact of Computational Histology AI Biomarkers on Clinical Management Decisions in Non-Muscle Invasive Bladder Cancer: A Multi-Center Real-World Study.

Vignesh T Packiam¹, Saum Ghodoussipour¹, Badrinath R Konety², Hamed Ahmadi³, Gautum Agarwal⁴, Lesli A Kiedrowski⁵, Viswesh Krishna⁵, Anirudh Joshi⁵, Stephen B Williams⁶, Armine K Smith⁷

¹Rutgers Cancer Institute, New Brunswick, NJ 08901, USA.

²Allina Health, Minneapolis, MN 55407, USA.

³Department of Urology, University of Minnesota, Minneapolis, MN 55455, USA.

⁴Mercy Clinic, St. Louis, MO 63141, USA.

⁵Valar Labs, Palo Alto, CA 94306, USA.

⁶Department of Surgery, Division of Urology, University of Texas Medical Branch, Galveston, TX 77555, USA.

⁷Brady Urological Institute, Johns Hopkins University, Washington, DC 21218, USA.

PubMed PMC DOI PDF

Abstract

Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette-Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG resp…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.