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Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: a Multi-Center Retrospective Study. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Patel, P., Harmon, S., Iseman, R., Ludkowski, O., Auman, H., Hawley, S., Newcomb, L. F., Lin, D. W., Nelson, P. S., Feng, Z., Boyer, H. D., Tretiakova, M. S., True, L. D., Vakar-Lopez, F., Carroll, P. R., Cooperberg, M. R., Chan, E., Simko, J., Fazli, L., Gleave, M., Hurtado-Coll, A., Thompson, I. M., Troyer, D., McKenney, J. K., Wei, W., Choyke, P. L., Bratslavsky, G., Turkbey, B., Siemens, D. R., Squire, J., Peng, Y. P., Brooks, J. D., Jamaspishvili, T. 2023: 100241

Abstract

Phosphatase and tensin homolog (PTEN) loss associates to adverse outcomes in prostate cancer and can be measured via immunohistochemistry (IHC). The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Post-surgical tissue microarray sections from the Canary Foundation (n=1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by pathologist and quantification of PTEN loss by AI (High-Risk AI-qPTEN) were significantly associated to shorted MFS in univariable analysis (cPTEN HR: 1.54, CI:1.07-2.21, p=0.019; AI-qPTEN HR: 2.55, CI:1.83,3.56), p<0.001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter metastasis-free survival (MFS) (HR:2.17, CI:1.49-3.17, p<0.001) and recurrence-free survival (HR:1.36, CI:1.06-1.75, p=0.016) when adjusting for relevant post-surgical clinical nomogram (CAPRA-S) while cPTEN does not show a statistically significant association (HR:1.33, CI:0.89-2, p=0.2 and HR:1.26, CI:0.99-1.62, p=0.063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR: 2.72, CI:1.46-5.06, p=0.002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy post-surgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding AI-qPTEN assessment workflow to clinical variables may affect post-operative surveillance or management options, particularly in low-risk patients.

View details for DOI 10.1016/j.modpat.2023.100241

View details for PubMedID 37343766