51. External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial.
- Author
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Ross AE, Zhang J, Huang HC, Yamashita R, Keim-Malpass J, Simko JP, DeVries S, Morgan TM, Souhami L, Dobelbower MC, McGinnis LS, Jones CU, Dess RT, Zeitzer KL, Choi K, Hartford AC, Michalski JM, Raben A, Gomella LG, Sartor AO, Rosenthal SA, Sandler HM, Spratt DE, Pugh SL, Mohamad O, Esteva A, Chen E, Schaeffer EM, Tran PT, and Feng FY
- Subjects
- Aged, Humans, Male, Middle Aged, Prognosis, Risk Assessment methods, Clinical Trials, Phase III as Topic, Randomized Controlled Trials as Topic, Artificial Intelligence, Prostatic Neoplasms pathology, Prostatic Neoplasms therapy
- Abstract
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., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)
- Published
- 2024
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