Development and International Evaluation of an Artificial Intelligence-based Model (PROGRxN-BCa) Using the World Health Organization 2004/2022 Grading System to Predict Progression Risk and Improve Substratification for Non-muscle-invasive Bladder Cancer

Jethro C C Kwong; Zizo Al-Daqqaq; Yashan Chelliahpillai; Soomin Lee; Kellie Kim; Maximiliano Ringa; Andrew Feifer; Katherine Lajkosz; Marian S Wettstein; Amy Chan; Taeweon Lee; Myky Nguyen; Wassim Kassouf; Peter C Black; Rodney H Breau; Michele Lodde; Adrian Fairey; Jean-Baptiste Lattouf; Claudio Jeldres; Ricardo Rendon; Nimira Alimohamed; Neil E Fleshner; Romain Diamand; Paolo Gontero; Richard J Sylvester; Bas W G van Rhijn; Ashish M Kamat; Alistair E W Johnson; Alexandre R Zlotta; Girish S Kulkarni; PROGRxN-BCa Consortium

doi:10.1016/j.eururo.2025.09.4145

Development and International Evaluation of an Artificial Intelligence-based Model (PROGRxN-BCa) Using the World Health Organization 2004/2022 Grading System to Predict Progression Risk and Improve Substratification for Non-muscle-invasive Bladder Cancer

Eur Urol. 2025 Oct 7:S0302-2838(25)04688-3. doi: 10.1016/j.eururo.2025.09.4145. Online ahead of print.

Authors

Jethro C C Kwong¹, Zizo Al-Daqqaq², Yashan Chelliahpillai², Soomin Lee², Kellie Kim², Maximiliano Ringa³, Andrew Feifer⁴, Katherine Lajkosz⁵, Marian S Wettstein⁶, Amy Chan⁷, Taeweon Lee⁸, Myky Nguyen⁹, Wassim Kassouf¹⁰, Peter C Black¹¹, Rodney H Breau¹², Michele Lodde¹³, Adrian Fairey¹⁴, Jean-Baptiste Lattouf¹⁵, Claudio Jeldres¹⁶, Ricardo Rendon¹⁷, Nimira Alimohamed¹⁸, Neil E Fleshner⁶, Romain Diamand⁹, Paolo Gontero¹⁹, Richard J Sylvester²⁰, Bas W G van Rhijn²¹, Ashish M Kamat⁸, Alistair E W Johnson²², Alexandre R Zlotta²³, Girish S Kulkarni²⁴; PROGRxN-BCa Consortium

Affiliations

¹ Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Division of Urology, Department of Surgery, University Health Network, Toronto, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada.
² Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
³ Division of Urology, Department of Surgery, Trillium Health Partners, Mississauga, Canada.
⁴ Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Division of Urology, Department of Surgery, University Health Network, Toronto, Canada; Division of Urology, Department of Surgery, Trillium Health Partners, Mississauga, Canada.
⁵ Division of Urology, Department of Surgery, University Health Network, Toronto, Canada.
⁶ Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Division of Urology, Department of Surgery, University Health Network, Toronto, Canada.
⁷ Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, Canada.
⁸ Department of Urology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁹ Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium.
¹⁰ Department of Urology, McGill University Health Centre, Montreal, Canada.
¹¹ Department of Urologic Sciences, University of British Columbia, Vancouver, Canada.
¹² Division of Urology, Department of Surgery, The Ottawa Hospital Research Institute, Ottawa, Canada.
¹³ Division of Urology, Department of Surgery, CHU de Québec-Université Laval, Quebec City, Canada.
¹⁴ Division of Urology, Department of Surgery, University of Alberta, Edmonton, Canada.
¹⁵ Division of Urology, Department of Surgery, Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
¹⁶ Division of Urology, Department of Surgery, Université de Sherbrooke, Sherbrooke, Canada.
¹⁷ Department of Urology, Dalhousie University, Halifax, Canada.
¹⁸ Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada.
¹⁹ Department of Urology, Città della Salute e della Scienza, University of Torino School of Medicine, Torino, Italy.
²⁰ Department of Biostatistics, European Organization for Research and Treatment of Cancer, Brussels, Belgium.
²¹ Department of Surgical Oncology (Urology), Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
²² Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada; Vector Institute, Toronto, Canada.
²³ Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Division of Urology, Department of Surgery, University Health Network, Toronto, Canada; Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, Canada.
²⁴ Division of Urology, Department of Surgery, University of Toronto, Toronto, Canada; Division of Urology, Department of Surgery, University Health Network, Toronto, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Canada. Electronic address: girish.kulkarni@uhn.ca.

PMID: 41062388
DOI: 10.1016/j.eururo.2025.09.4145

Abstract

Background and objective: Current non-muscle-invasive bladder cancer (NMIBC) tools perform suboptimally in predicting progression risk to potentially lethal muscle-invasive disease. We aimed to improve risk assessment using artificial intelligence approaches (PROGression Risk assessment in NMIBC [PROGRxN-BCa]).

Methods: PROGRxN-BCa was trained using 14 clinicopathological features on 3324 NMIBC patients treated from 2005 to 2022 at four Canadian institutions. External testing was performed on 9335 patients treated from 2005 to 2023 across 30 North American and European institutions. The primary outcome was time to progression (muscle-invasive or metastatic disease). PROGRxN-BCa was compared with the European Association of Urology (EAU) risk calculator. Performance was characterised using concordance index (c-index), calibration plots, instability assessments, decision curve analysis, and an algorithmic audit.

Key findings and limitations: During a median follow-up of 3.3 yr (interquartile range 1.6-5.8), 1405 of 12659 patients progressed. In the external testing cohort, PROGRxN-BCa had significantly higher c-index (0.79, 95% confidence interval [CI] 0.77-0.80) and net benefit overall and across different subgroups compared with the EAU risk calculator (0.71, 95% CI 0.70-0.73, p < 0.001). This improvement was consistent regardless of treatment with bacillus Calmette-Guérin, adherence to guideline-concordant care, and World Health Organization 1973 or 2004/2022 grading system, and was consistent among patients with at least 5 yr of follow-up. It also outperformed other guideline-endorsed tools and a previously published artificial intelligence model. Compared with guideline recommendations, PROGRxN-BCa improved substratification of intermediate-risk patients into distinct risk tertiles, with estimated 5-yr progression risks of 2%, 7%, and 15%-the latter in line with high-risk NMIBC.

Conclusions and clinical implications: PROGRxN-BCa outperformed current tools in the largest NMIBC cohort of its kind. Its integration into guidelines could improve risk stratification and patient management.

Keywords: Artificial intelligence; Machine learning; Non–muscle-invasive bladder cancer; Progression; Risk stratification.