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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

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dc.contributor.authorLotan, Yair
dc.contributor.authorKrishna, Viswesh
dc.contributor.authorAbuzeid, Waleed
dc.contributor.authorLauner, Bryn
dc.contributor.authorChang, Sam
dc.contributor.authorKrishna, Vrishab
dc.contributor.authorShingi, Siddhant
dc.contributor.authorGordetsk, Jennifer
dc.contributor.authorGerald, Thomas
dc.contributor.authorWoldu, Solomon
dc.contributor.authorShkolyar, Eugene
dc.contributor.authorHayne, Dickon
dc.contributor.authorRedfern, Andrew
dc.contributor.authorSpalding, Lisa
dc.contributor.authorStewart, Courtney
dc.contributor.authorEyzaguirre, Eduardo
dc.contributor.authorImtiaz, Shamsunnahar
dc.contributor.authorNarayan, Vikram
dc.contributor.authorPackiam, Vignesh
dc.contributor.authorO'Donnell, Michael
dc.contributor.authorLi, Roger
dc.contributor.authorBaekelandt, Loic
dc.contributor.authorJoniau, Steven
dc.contributor.authorZuiverloon. Tahlita
dc.contributor.authorFernandez, Mario
dc.contributor.authorSchultz, Marcela
dc.contributor.authorHensley, Patrick
dc.contributor.authorAllison, Derek
dc.contributor.authorTaylor, John
dc.contributor.authorHamza, Ameer
dc.contributor.authorKamat, Ashish
dc.contributor.authorNimgaonkar, Vivek
dc.contributor.authorSonawane, Snehal
dc.contributor.authorMiller, Daniel
dc.contributor.authorWatson, Drew
dc.contributor.authorVrabac, Damir
dc.contributor.authorJoshi, Anirudh
dc.contributor.authorShah, Jay
dc.contributor.authorWilliams, Stephen
dc.date.accessioned2026-09-11T17:48:30Z
dc.date.available2026-09-11T17:48:30Z
dc.date.issued2025
dc.description.abstractPurpose: There 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 methods: Pre-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. Results: Nine 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, P < .0001). "High progression risk" patients had poorer progression-free survival (HR, 3.87, P < .001) and higher risk of cystectomy (HR, 3.35, P < .001) than "low progression risk" patients. Cases harboring the BCG-unresponsive disease signature had a shorter time to development of BCG-unresponsive disease than cases without the signature (HR, 2.31, P < .0001). AI assays provided predictive information beyond clinicopathologic factors. Conclusions: We 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
dc.description.versionVersión Publicada
dc.identifier.citationLotan, Y., Krishna, V., Abuzeid, W. M., Launer, B., Chang, S. S., Krishna, V., Shingi, S., Gordetsky, J. B., Gerald, T., Woldu, S., Shkolyar, E., Hayne, D., Redfern, A., Spalding, L., Stewart, C., Eyzaguirre, E., Imtiaz, S., Narayan, V. M., Packiam, V. T., O'Donnell, M. A., … Williams, S. B. (2025). 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. The Journal of urology, 213(2), 192–204. https://doi.org/10.1097/JU.0000000000004278
dc.identifier.doihttps://doi.org/10.1097/JU.0000000000004278
dc.identifier.urihttps://hdl.handle.net/11447/11122
dc.language.isoen
dc.subjectArtificial intelligence
dc.subjectBladder cancer
dc.subjectProgression
dc.subjectRecurrence
dc.titlePredicting 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
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceThe Journal of urology
dspace.entity.typePublication

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