Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study

Palak Patel; Stephanie Harmon; Rachael Iseman; Olga Ludkowski; Heidi Auman; Sarah Hawley; Lisa F Newcomb; Daniel W Lin; Peter S Nelson; Ziding Feng; Hilary D Boyer; Maria S Tretiakova; Larry D True; Funda Vakar-Lopez; Peter R Carroll; Matthew R Cooperberg; Emily Chan; Jeff Simko; Ladan Fazli; Martin Gleave; Antonio Hurtado-Coll; Ian M Thompson; Dean Troyer; Jesse K McKenney; Wei Wei; Peter L Choyke; Gennady Bratslavsky; Baris Turkbey; D Robert Siemens; Jeremy Squire; Yingwei P Peng; James D Brooks; Tamara Jamaspishvili

doi:10.1016/j.modpat.2023.100241

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study

Mod Pathol. 2023 Oct;36(10):100241. doi: 10.1016/j.modpat.2023.100241. Epub 2023 Jun 19.

Authors

Palak Patel¹, Stephanie Harmon², Rachael Iseman³, Olga Ludkowski⁴, Heidi Auman⁵, Sarah Hawley⁵, Lisa F Newcomb⁶, Daniel W Lin⁶, Peter S Nelson⁷, Ziding Feng⁸, Hilary D Boyer⁸, Maria S Tretiakova⁹, Larry D True⁹, Funda Vakar-Lopez⁹, Peter R Carroll¹⁰, Matthew R Cooperberg¹⁰, Emily Chan¹⁰, Jeff Simko¹¹, Ladan Fazli¹², Martin Gleave¹², Antonio Hurtado-Coll¹², Ian M Thompson¹³, Dean Troyer¹⁴, Jesse K McKenney¹⁵, Wei Wei¹⁵, Peter L Choyke¹⁶, Gennady Bratslavsky¹⁷, Baris Turkbey², D Robert Siemens¹⁸, Jeremy Squire¹⁹, Yingwei P Peng²⁰, James D Brooks²¹, Tamara Jamaspishvili²²

Affiliations

¹ Department of Cell Biology at The Arthur and Sonia Labatt Brain Tumour Research Centre at the Hospital for Sick Children, Toronto, Ontario, Canada.
² Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland; Artificial Intelligence Resource, National Cancer Institute, Bethesda, Maryland.
³ Division of Cancer Biology and Genetics, Queen's University, Kingston, Ontario, Canada.
⁴ University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
⁵ Canary Foundation, Woodside, California.
⁶ Department of Urology, University of Washington Medical Center, Seattle, Washington.
⁷ Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington.
⁸ Program of Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
⁹ Department of Pathology, University of Washington Medical Center, Seattle, Washington.
¹⁰ Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California.
¹¹ Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California; Department of Pathology, University of California San Francisco, San Francisco, California.
¹² The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada.
¹³ CHRISTUS Medical Center Hospital, San Antonio, Texas.
¹⁴ Department of Pathology, Eastern Virginia Medical School, Norfolk, Virginia; Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia.
¹⁵ Department of Pathology, Cleveland Clinic, Cleveland, Ohio.
¹⁶ Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland.
¹⁷ Department of Urology, SUNY Upstate Medical University, Syracuse, New York.
¹⁸ Department of Urology, Queen's University, Kingston, Ontario, Canada.
¹⁹ Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
²⁰ Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada; Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada.
²¹ Department of Urology, Stanford University Medical Center, Stanford, California.
²² Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada; Department of Pathology and Molecular Medicine, SUNY Upstate Medical University, Syracuse, New York. Electronic address: jamaspit@upstate.edu.

PMID: 37343766
PMCID: PMC10592257 (available on 2024-10-01)
DOI: 10.1016/j.modpat.2023.100241

Abstract

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. 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. Postsurgical 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 the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .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 = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. 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 the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Keywords: artificial intelligence; metastasis; prostate cancer; quantitative PTEN loss; risk stratification.

Grants and funding

Z99 CA999999/ImNIH/Intramural NIH HHS/United States