Biochemical recurrence (BCR) after radical prostatectomy (RP) is a heterogeneous disease state in prostate cancer with multiple treatment options. Improved risk stratification could enable more personalized decision-making. We developed and validated a digital pathology-based multimodal artificial intelligence (MMAI) model to predict outcomes in post-RP BCR patients undergoing salvage therapy.
An MMAI model was trained to predict distant metastasis (DM) using prostate histopathology image features and clinical variables (pathologic grade group, pathologic T stage, prostate-specific antigen level before salvage radiotherapy [SRT], age, and surgical margin). The locked model was validated in 533 patients from NRG/RTOG 9601 and 0534 treated with SRT ± hormone therapy (HT), using Cox regression and time-dependent area under the receiver operating characteristic curve.
With a median follow-up of 9.3 yrs, MMAI score was significantly associated with DM (subdistribution hazard ratio = 2.17 per standard deviation [95% confidence interval 1.65-2.85]; p < 0.001) and remained independently prognostic after adjusting for clinical variables and treatment. The 10-yr time-dependent area under the receiver operating characteristic curve for MMAI was 0.74 compared with 0.68 for a clinical nomogram. Binary risk categorization demonstrated higher 10-yr DM incidence in the MMAI high-risk (25%) than in the low-risk (8.8%) group. The absolute reduction in 10-yr DM incidence with HT plus SRT versus SRT alone was 21% in the high-risk group versus 2.5% in the low-risk group. Limitations include the use of archived trial cohorts.
The post-RP MMAI model provides individualized risk estimates after SRT ± HT and may support shared decision-making about salvage treatment. External and prospective validation are ongoing.
European urology. 2025 Dec 22 [Epub ahead of print]
Todd M Morgan, Yi Ren, Siyi Tang, Wouter Zwerink, Emmalyn Chen, Akinori Mitani, Huei-Chung Huang, Jeffry P Simko, Sandy DeVries, Alan Pollack, Derek Wilke, André-Guy Martin, Alexander G Balogh, Jeff M Michalski, Michael J Greenberg, Jason A Efstathiou, Jean-Paul Bahary, Ashley E Ross, Andre Esteva, Trevor J Royce, Paul L Nguyen, Karen E Hoffman, Howard M Sandler, Phuoc T Tran, Stephanie L Pugh, Felix Y Feng, Daniel E Spratt
University of Michigan Comprehensive Cancer Center, Ann Arbor, MI, USA. Electronic address: ., Artera Inc., Los Altos, CA, USA., NRG Oncology Biospecimen Bank, San Francisco, CA, USA; UCSF Medical Center-Mission Bay, San Francisco, CA, USA., University of Miami Miller School of Medicine, Miami, FL, USA., Capital Health - Nova Scotia Cancer Centre, Halifax, NS, Canada., CHU de Québec- Université Laval, Quebec, QC, Canada., Tom Baker Cancer Centre, Calgary, AB, Canada., Washington University School of Medicine, St Louis, MO, USA., Thomas Jefferson University Hospital, Philadelphia, PA, USA., Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Centre Hospitalier de l'Université de Montréal-Notre Dame, Montreal, QC, Canada., Northwestern University, Chicago, IL, USA., Dana-Farber Cancer Institute, Boston, MA, USA., MD Anderson Cancer Center, Houston, TX, USA., Cedars-Sinai Medical Center, Los Angeles, CA, USA., University of Maryland Medical System, Baltimore, MD, USA., NRG Oncology Statistics and Data Management Center, Philadelphia, PA, USA; American College of Radiology, Philadelphia, PA, USA., UCSF Medical Center-Mission Bay, San Francisco, CA, USA., University Hospitals at Case Western Reserve University, Cleveland, OH, USA. Electronic address: .