ArticleUrologic oncology2026
Development and validation of a computational histology artificial intelligence-powered prognostic biomarker in muscle-invasive bladder cancer.
Article in Urologic oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPatients 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.
methodsThe 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).
resultsA 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).
conclusionsWe 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.
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