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Updated: Aug 12, 2026
Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
Published on: March 29, 2019
Development and validation of a computational histology artificial intelligence-powered prognostic biomarker in
Yair Lotan1, Vitaly Margulis1, Solomon Woldu1
1Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX.
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 H&E-stained whole slide images. The Cancer Genome Atlas was used for development to construct a signature of features associated with the primary endpoint of recurrence-free survival (RFS). A continuous risk score was dichotomized into favorable and unfavorable groups. For validation, the performance of the locked model was then assessed in an independent, held-out, retrospective, pooled real-world data cohort of patients from NCI-Designated Cancer Centers with cT2N0M0 urothelial carcinoma who underwent radical cystectomy with/without neoadjuvant chemotherapy (NAC).
Results:
A total of 178 patients were included: 44 in development and 134 in validation, of whom 50% received NAC. In validation, those classified as unfavorable risk by the CHAI biomarker (N = 67) had worse RFS (HR 3.1 [1.7-5.7], P < 0.001), cancer-specific survival (CSS) (3.5 [1.5-7.8], P = 0.003), and overall survival (OS) (3.0, [1.5-5.7], P = 0.001) vs. favorable risk (N = 67). Three-year RFS was 40% vs. 74% for disease classified as unfavorable and favorable risk, respectively (P < 0.001). After adjusting for prognostic clinical variables, including receipt of NAC, the biomarker remained associated with RFS, CSS, and OS (P < 0.01). Exploratory analysis found a significant interaction between the biomarker and NAC for RFS (P = 0.02).
Conclusions:
We developed and validated an image-only AI-based biomarker from pre-treatment H&E TURBT specimens associated with clinical outcomes in cT2 MIBC. While future development and validation work is warranted, these hypothesis-generating retrospective findings support the potential of this approach to advancing precision medicine in MIBC.
