External validation of a digital pathology-based multimodal artificial intelligence architecture in the NRG/RTOG 9902 phase 3 trial
Open accessAbstract
Background Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.Objective To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.Design, setting, and participants Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality).Outcome measurements and statistical analysis Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models.Results and limitations The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm.Conclusions We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care.Patient summary This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer.
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The Esteva et al. (2022) model was externally validated in a phase 3 trial (NRG/RTOG 9902) and demonstrated significant and independent association with prognostic factors compared to current methods such as NCCN high-risk features.
“In addition, this model was externally validated in a phase 3 trial (NRG/RTOG 9902) and demonstrated significant and independent association with prognostic factors compared to current methods such as the National Comprehensive Cancer Network (NCCN) high-risk features (Ross et al., 2024).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Ross et al., 2024
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- Authors
- Ross, A. E., Zhang, J., Huang, H.-C., Yamashita, R., Keim-Malpass, J., Simko, J. P., DeVries, S., Morgan, T. M., Souhami, L., Dobelbower, M. C., McGinnis, L. S., Jones, C. U., Dess, R. T., Zeitzer, K. L., Choi, K., Hartford, A. C., Michalski, J. M., Raben, A., Gomella, L. G., Sartor, A. O., Rosenthal, S. A., Sandler, H. M., Spratt, D. E., Pugh, S. L., Mohamad, O., Esteva, A., Chen, E., Schaeffer, E. M., Tran, P. T., & Feng, F. Y.
- Affiliations
- Northwestern Medicine (US)
- University of California, San Francisco (US)
- University of Michigan (US)
- University of Alabama at Birmingham (US)
- Novant Health Presbyterian Medical Center (US)
- Sutter Medical Center (US)
- Jefferson Einstein Philadelphia Hospital (US)
- SUNY Downstate Health Sciences University (US)
- Dartmouth–Hitchcock Medical Center (US)
- Washington University in St. Louis (US)
- Christiana Care Health System (US)
- Thomas Jefferson University Hospital (US)
- Cedars-Sinai Medical Center (US)
- University Hospitals Seidman Cancer Center (US)
- American College of Radiology (US)
- Northwestern University (US)
- University of Maryland, Baltimore (US)
- Corresponding author
- Ross, Ashley E.
- ORCID
- Ross, Ashley E.
- Yamashita, Rikiya
- Morgan, Todd M.
- Dobelbower, Michael C.
- Jones, Christopher U.
- Dess, Robert T.
- Zeitzer, Kenneth L.
- Choi, Kwang
- Hartford, Alan C.
- Michalski, Jeff M.
- Raben, Adam
- Gomella, Leonard G.
- Rosenthal, Seth A.
- Sandler, Howard M.
- Spratt, Daniel E.
- Pugh, Stephanie L.
- Mohamad, Osama
- Esteva, Andre
- Chen, Emmalyn
- Schaeffer, Edward M.
- Tran, Phuoc T.
- Feng, Felix Y.
- Venue
- European Urology Oncology
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 7
- Issue
- 5
- Pages
- 1024-1033
- ISSN
- 2588-9311 (ISSN-L)
- PMC
- PMC11289167
- OpenAlex
- W4391426559
- Cited by
- 35
- References
- 22
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 4 · 2023: 2
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- European Urology Oncology (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- Funders
- Amgen
- Bristol-Myers Squibb
- Pfizer
- AstraZeneca
- Sanofi
- Varian Medical Systems
- Endocyte
- NRG Oncology (U24CA196067, U10 CA180822, UG1CA189867, U10CA180868)
- Advanced Accelerator Applications
- Invitae
- Astellas Pharma
- EMD Serono
- University of California, San Francisco
- Foundation Medicine
- National Cancer Institute (UG1CA233160, UG1CA189867, U10CA180868, U54CA273956, U24CA196067, U10CA180822)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Keywords
- localized prostate cancer, high-risk prostate cancer, locally advanced prostate cancer, multimodal AI, digital histopathology, external validation, risk stratification, distant metastasis, prostate cancer-specific mortality, prognostic model
- MeSH terms
- Aged, Artificial Intelligence, Humans, Male, Middle Aged, Prognosis, Prostatic Neoplasms, Randomized Controlled Trials as Topic, Clinical Trials, Phase III as Topic, Risk Assessment
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