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.
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.
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).
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.
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.
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.
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.
European urology oncology. 2024 Jan 31 [Epub ahead of print]
Ashley E Ross, Jingbin Zhang, Huei-Chung Huang, Rikiya Yamashita, Jessica Keim-Malpass, Jeffry P Simko, Sandy DeVries, Todd M Morgan, Luis Souhami, Michael C Dobelbower, L Scott McGinnis, Christopher U Jones, Robert T Dess, Kenneth L Zeitzer, Kwang Choi, Alan C Hartford, Jeff M Michalski, Adam Raben, Leonard G Gomella, A Oliver Sartor, Seth A Rosenthal, Howard M Sandler, Daniel E Spratt, Stephanie L Pugh, Osama Mohamad, Andre Esteva, Emmalyn Chen, Edward M Schaeffer, Phuoc T Tran, Felix Y Feng
Department of Urology, Northwestern Medicine, Chicago, IL, USA. Electronic address: ., Artera Inc., Los Altos, CA, USA., University of California San Francisco, San Francisco, CA, USA., University of Michigan, Ann Arbor, MI, USA., The Research Institute of the McGill University Health Centre (MUHC), Montreal, QC, Canada., University of Alabama at Birmingham Cancer Center, Birmingham, AL, USA., Novant Health Presbyterian Medical Center, Charlotte, NC, USA., Sutter Medical Center Sacramento, Sacramento, CA, USA., Albert Einstein Medical Center, Philadelphia, PA, USA., Brooklyn MB-CCOP/SUNY Downstate, Brooklyn, NY, USA., Dartmouth Hitchcock Medical Center, Lebanon, NH, USA., Washington University School of Medicine, Saint Louis, MO, USA., Christiana Care Health Services, Inc. CCOP, Wilmington, DE, USA., Thomas Jefferson University Hospital, Philadelphia, PA, USA., Tulane University Health Sciences Center, New Orleans, LA, USA., Cedars-Sinai Medical Center, Los Angeles, CA, USA., UH Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA., NRG Oncology Statistics and Data Management Center and American College of Radiology, Philadelphia, PA, USA., Northwestern University, Chicago, IL, USA., University of Maryland, Baltimore, MD, USA.