(UroToday.com) The American Urological Association (AUA) 2025 Annual Meeting, held in Las Vegas, NV, was host to a non-invasive bladder cancer interactive poster session. Dr. Jethro Kwong presented PROGRxN-BCa, an artificial intelligence (AI)-based model to predict progression risk in non-muscle invasive bladder cancer (NMIBC) and improve the sub-stratification of intermediate-risk disease NMIBC patients.
Accurate prediction of tumour progression is vital to inform patient counselling, intensify treatment when appropriate, and consider eligibility for clinical trials. However, there are numerous limitations with both clinical and AI tools available today. The aim of this study was to overcome these limitations by developing a robust, internationally developed AI model to predict NMIBC progression.
This is the largest NMIBC cohort to date (n=12,659). This model was developed using a training set of 3,324 patients treated at 4 academic or community hospitals between 2005 and 2022. It underwent external validation using a cohort of 9,335 patients treated at 30 North American and European institutions between 2005 and 2023.
PROGRxN-BCa was based on a random survival forest, and it was compared against the current guideline-endorsed EAU risk calculator for the outcome of time to progression to muscle-invasive or metastatic disease.
The study investigators found that:
- PROGRxN-BCa AI model outperformed the other models based on overall c-index and across clinically relevant subgroups, generally around 10% better
- What’s notable is that this model was also consistently better irrespective of whether guideline-concordant care was followed (e.g., repeat TURBT, BCG for T1)
- This model (shown by the blue line) is also well calibrated and has a higher net benefit on decision curve analysis.
The study investigators also looked at the sub-stratification of intermediate risk patients. Current guidelines recommend counting the number of intermediate risk factors, and as shown below on the left, it is challenging to distinguish patients with 0 risk factors from those with 1-2. However, with this AI model, patients can be separated into distinct risk tertiles.
In conclusion, Dr. Kwong and colleagues demonstrated in the largest NMIBC cohort to date that this AI model outperforms currently available risk stratification tools and greatly improves the sub-stratification of the intermediate-risk group.
Presented by: Jethro Kwong, MD, MSc, Resident Physician, Division of Urology, University of Toronto, Toronto, ON
Written by: Rashid K. Sayyid, MD, MSc – Robotic Urologic Oncology Fellow at The University of Southern California, @rksayyid on Twitter during the 2025 American Urological Association (AUA) annual meeting held in Las Vegas, NV, Saturday, April 26 - Tuesday, April 29, 2025
Related content: International Validation of AI Tool for Stratifying Bladder Cancer Progression Risk - Jethro Kwong