ArticleThe Journal of urology2025
Predicting Response to Intravesical Bacillus Calmette-Guérin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12-Center Cohort.
Article in The Journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Development and Validation of a Computational Histology Artificial Intelligence-Powered Biomarker in Metastatic Hormone-Sensitive Prostate Cancer on Randomized Phase III Trials.JCO precision oncology · 2026Trial
- Re: Nielsen N.K, Hjort P.B, Hansen E.B, et al. Substaging and stratified treatment of T1 bladder cancer - a nationwide register‑based study. Eur Urol Open Sci 2026;90:92‑100.European urology open science · 2026Article
- Development and validation of a computational histology artificial intelligence-powered prognostic biomarker in muscle-invasive bladder cancer.Urologic oncology · 2026Article
- Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.World journal of oncology · 2026Article
- Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Article
- Integrating clinically actionable biomarkers into bladder cancer care - recommendations from the International Bladder Cancer Group.Nature reviews. Urology · 2026Review
- Review
- 'BJUI Clinical Dilemma': Recurrent high-grade non-muscle-invasive bladder cancer in 2026.BJU international · 2026Article
- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
- Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.NPJ precision oncology · 2026Article
- What's new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration.Journal of pathology and translational medicine · 2026Article
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Innovative AI model for bladder cancer diagnosis.Discover oncology · 2025Article
- Presence of an Artificial Intelligence-powered Predictive Biomarker Is Associated with a Poor Response to Intravesical Bacillus Calmette-Guerin but Not to Intravesical Sequential Gemcitabine/Docetaxel in Patients with High-grade Non-muscle-invasive Bladder Cancer.European urology oncology · 2025Article
- Article
- Artificial Intelligence Biomarkers Predict Poor Efficacy of Bacillus Calmette-Guérin Rechallenge in Previously Bacillus Calmette-Guérin-Treated Nonmuscle-Invasive Bladder Cancer.The Journal of urology · 2025Article
- Editorial Comment.The Journal of urology · 2025Article
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39 authors.
Funding
Abstract
purposeThere are few markers to identify those likely to recur or progress after treatment with intravesical bacillus Calmette-Guérin (BCG). We developed and validated artificial intelligence (AI)-based histologic assays that extract interpretable features from transurethral resection of bladder tumor digitized pathology images to predict risk of recurrence, progression, development of BCG-unresponsive disease, and cystectomy. MATERIALS AND
methodsPre-BCG resection-derived whole-slide images and clinical data were obtained for high-risk nonmuscle-invasive bladder cancer cases treated with BCG from 12 centers and were analyzed through a segmentation and feature extraction pipeline. Features associated with clinical outcomes were defined and tested on independent development and validation cohorts. Cases were classified into high or low risk for recurrence, progression, BCG-unresponsive disease, and cystectomy.
resultsNine hundred forty-four cases (development: 303, validation: 641, median follow-up: 36 months) representative of the intended use population were included (high-grade Ta: 34.1%, high-grade T1: 54.8%; carcinoma in situ only: 11.1%, any carcinoma in situ: 31.4%). In the validation cohort, "high recurrence risk" cases had inferior high-grade recurrence-free survival vs "low recurrence risk" cases (HR, 2.08,
conclusionsWe developed and validated AI-based histologic assays that identify high-risk nonmuscle-invasive bladder cancer cases at higher risk of recurrence, progression, BCG-unresponsive disease, and cystectomy, potentially aiding clinical decision making.
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