Peer-Reviewed Publication
Urol Oncol2026August 10, 2026Journal Article
Development and validation of a computational histology artificial intelligence-powered prognostic biomarker in muscle-invasive bladder cancer.
Yair Lotan1, Vitaly Margulis1, Solomon Woldu1, Derek Allison2, Joon Kyung Kim3, Laura Bukavina4, Sam S Chang5, Vrishab Krishna6, Gaurav Kaul6, Akshay Neema6, Haochen Zhang6, Trevor J Royce7, Viswesh Krishna6, Anirudh Joshi6, Ashish M Kamat8, Roger Li9, Patrick J Hensley3
1Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX.
2Department of Pathology, University of Kentucky, Lexington, KY.
3Department of Urology, University of Kentucky, Lexington, KY.
4Department of Urology, Cleveland Clinic, Cleveland, OH.
5Department of Urology, Vanderbilt University Medical Center, Nashville, TN.
6Valar Labs, Palo Alto, CA.
7Valar Labs, Palo Alto, CA; Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC. Electronic address: trevor.royce@mail.harvard.edu.
8Department of Urology, MD Anderson Cancer Center, Houston, TX.
9Urology, Moffitt Cancer Center, Tampa, FL.
Abstract
BACKGROUND: Patients with muscle-invasive bladder cancer (MIBC) have heterogeneous outcomes following transurethral resection of bladder tumor (TURBT). We used a computational histopathology artificial intelligence (CHAI)-based platform to develop and validate a digital image-only MIBC prognostic biomarker.
METHODS: The CHAI platform extracts histologic features from pre-treatment TURBT specimen…
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