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

Yair Lotan; Viswesh Krishna; Waleed M. Abuzeid; Bryn Launer; Sam S. Chang; Vrishab Krishna; Siddhant Shingi; Jennifer B. Gordetsky; Thomas Gerald; Solomon Woldu; Eugene Shkolyar; Dickon Hayne; Andrew Redfern; Lisa Spalding; Courtney Stewart; Eduardo Eyzaguirre; Shamsunnahar Imtiaz; Vikram M. Narayan; Vignesh T. Packiam; Michael A. O'Donnell; Roger Li; Loic Baekelandt; Steven Joniau; Tahlita Zuiverloon; Mario I. Fernandez; Marcela Schultz; Patrick J. Hensley; Derek Allison; John A. Taylor; Ameer Hamza; Ashish Kamat; Vivek Nimgaonkar; Snehal Sonawane; Daniel L. Miller; Drew Watson; Damir Vrabac; Anirudh Joshi; Jay B. Shah; Stephen B. Williams

doi:10.1097/JU.0000000000004278

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

Yair Lotan, Viswesh Krishna, Waleed M. Abuzeid, Bryn Launer, Sam S. Chang, Vrishab Krishna, Siddhant Shingi, Jennifer B. Gordetsky, Thomas Gerald, Solomon Woldu, Eugene Shkolyar, Dickon Hayne, Andrew Redfern, Lisa Spalding, Courtney Stewart, Eduardo Eyzaguirre, Shamsunnahar Imtiaz, Vikram M. Narayan, Vignesh T. Packiam, Michael A. O'DonnellRoger Li, Loic Baekelandt, Steven Joniau, Tahlita Zuiverloon, Mario I. Fernandez, Marcela Schultz, Patrick J. Hensley, Derek Allison, John A. Taylor, Ameer Hamza, Ashish Kamat, Vivek Nimgaonkar, Snehal Sonawane, Daniel L. Miller, Drew Watson, Damir Vrabac, Anirudh Joshi, Jay B. Shah, Stephen B. Williams

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Purpose: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.

Original language	English
Pages (from-to)	192-204
Number of pages	13
Journal	Journal of Urology
Volume	213
Issue number	2
DOIs	https://doi.org/10.1097/JU.0000000000004278
State	Published - Feb 1 2025

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Published on behalf of the American Urological Association, Education and Research, Inc.

Funding

Dr Lotan\u2014consultant (Valar Labs, Pacific Edge, Photocure, Astrazeneca, Vessi Medical, Nucelix, Merck, Engene, CAPs Medical, C2I Genomics, Fergene, Abbvie, Ambu, Seattle Genetics, Verity Pharmaceuticals, Urogen, Stimit, Nanorobot, Convergent Genomics, Aura Biosciences, Nonagen, Pfizer, Phinomics, CG Oncology, Virtuoso Surgical, Xcures, Uroviu, On Target Lab, Promis Diagnostics, Uroessentials); Dr Chang\u2014consultant (Valar Labs); Dr Gordetsky\u2014consultant (Janssen); Dr Shkolyar\u2014consultant (Johnson & Johnson); Dr Hayne\u2014consultant (Merck, Pfizer, Urogen, BMS), clinical trial support (Valar Labs, Medical Developments International, Astrazeneca); Dr Packiam\u2014consultant (Valar Labs); Dr Fernandez\u2014speaker bureau (Grunenthal); advisory board, (Janssen, Tecnofarma); Dr Shah\u2014consultant (Valar Labs); Dr Williams\u2014consultant (Valar Labs, Janssen, Merck, Provepharm, Photocure), advisory board (BJUI).

Funders	Funder number
BJUI

Keywords

artificial intelligence
bladder cancer
progression
recurrence

ASJC Scopus subject areas

Urology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Lotan, 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., ... 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. Journal of Urology, 213(2), 192-204. https://doi.org/10.1097/JU.0000000000004278

@article{34414041aeb0476aa34045c6a0608685,

title = "Predicting Response to Intravesical Bacillus Calmette-Gu{\'e}rin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay: Development and Validation in an International 12-Center Cohort",

abstract = "Purpose:There are few markers to identify those likely to recur or progress after treatment with intravesical bacillus Calmette-Gu{\'e}rin (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.",

keywords = "artificial intelligence, bladder cancer, progression, recurrence",

author = "Yair Lotan and Viswesh Krishna and Abuzeid, \{Waleed M.\} and Bryn Launer and Chang, \{Sam S.\} and Vrishab Krishna and Siddhant Shingi and Gordetsky, \{Jennifer B.\} and Thomas Gerald and Solomon Woldu and Eugene Shkolyar and Dickon Hayne and Andrew Redfern and Lisa Spalding and Courtney Stewart and Eduardo Eyzaguirre and Shamsunnahar Imtiaz and Narayan, \{Vikram M.\} and Packiam, \{Vignesh T.\} and O'Donnell, \{Michael A.\} and Roger Li and Loic Baekelandt and Steven Joniau and Tahlita Zuiverloon and Fernandez, \{Mario I.\} and Marcela Schultz and Hensley, \{Patrick J.\} and Derek Allison and Taylor, \{John A.\} and Ameer Hamza and Ashish Kamat and Vivek Nimgaonkar and Snehal Sonawane and Miller, \{Daniel L.\} and Drew Watson and Damir Vrabac and Anirudh Joshi and Shah, \{Jay B.\} and Williams, \{Stephen B.\}",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Published on behalf of the American Urological Association, Education and Research, Inc.",

year = "2025",

month = feb,

day = "1",

doi = "10.1097/JU.0000000000004278",

language = "English",

volume = "213",

pages = "192--204",

number = "2",

}

Lotan, Y, Krishna, V, Abuzeid, WM, Launer, B, Chang, SS, Krishna, V, Shingi, S, Gordetsky, JB, Gerald, T, Woldu, S, Shkolyar, E, Hayne, D, Redfern, A, Spalding, L, Stewart, C, Eyzaguirre, E, Imtiaz, S, Narayan, VM, Packiam, VT, O'Donnell, MA, Li, R, Baekelandt, L, Joniau, S, Zuiverloon, T, Fernandez, MI, Schultz, M, Hensley, PJ , Allison, D, Taylor, JA, Hamza, A, Kamat, A, Nimgaonkar, V, Sonawane, S, Miller, DL, Watson, D, Vrabac, D, Joshi, A, Shah, JB & Williams, SB 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', Journal of Urology, vol. 213, no. 2, pp. 192-204. https://doi.org/10.1097/JU.0000000000004278

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. / Lotan, Yair; Krishna, Viswesh; Abuzeid, Waleed M. et al.
In: Journal of Urology, Vol. 213, No. 2, 01.02.2025, p. 192-204.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Predicting Response to Intravesical Bacillus Calmette-Guérin in High-Risk Nonmuscle-Invasive Bladder Cancer Using an Artificial Intelligence-Powered Pathology Assay

T2 - Development and Validation in an International 12-Center Cohort

AU - Lotan, Yair

AU - Krishna, Viswesh

AU - Abuzeid, Waleed M.

AU - Launer, Bryn

AU - Chang, Sam S.

AU - Krishna, Vrishab

AU - Shingi, Siddhant

AU - Gordetsky, Jennifer B.

AU - Gerald, Thomas

AU - Woldu, Solomon

AU - Shkolyar, Eugene

AU - Hayne, Dickon

AU - Redfern, Andrew

AU - Spalding, Lisa

AU - Stewart, Courtney

AU - Eyzaguirre, Eduardo

AU - Imtiaz, Shamsunnahar

AU - Narayan, Vikram M.

AU - Packiam, Vignesh T.

AU - O'Donnell, Michael A.

AU - Li, Roger

AU - Baekelandt, Loic

AU - Joniau, Steven

AU - Zuiverloon, Tahlita

AU - Fernandez, Mario I.

AU - Schultz, Marcela

AU - Hensley, Patrick J.

AU - Allison, Derek

AU - Taylor, John A.

AU - Hamza, Ameer

AU - Kamat, Ashish

AU - Nimgaonkar, Vivek

AU - Sonawane, Snehal

AU - Miller, Daniel L.

AU - Watson, Drew

AU - Vrabac, Damir

AU - Joshi, Anirudh

AU - Shah, Jay B.

AU - Williams, Stephen B.

PY - 2025/2/1

Y1 - 2025/2/1

N2 - Purpose: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.

AB - Purpose: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.

KW - artificial intelligence

KW - bladder cancer

KW - progression

KW - recurrence

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DO - 10.1097/JU.0000000000004278

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JO - Journal of Urology

JF - Journal of Urology

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Lotan Y, Krishna V, Abuzeid WM, Launer B, Chang SS, Krishna V et al. 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. Journal of Urology. 2025 Feb 1;213(2):192-204. doi: 10.1097/JU.0000000000004278

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

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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