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

Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
PMID: 37343766

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.

Authors

  • Palak Patel
    Louisiana State University Health Sciences Center, Department of Otolaryngology, Head and Neck Surgery, New Orleans, Louisiana, USA. Electronic address: ppat13@lsuhsc.edu.
  • Stephanie Harmon
  • Rachael Iseman
    Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada.
  • Olga Ludkowski
    University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada.
  • Heidi Auman
    Canary Foundation, Woodside, California.
  • Sarah Hawley
    Canary Foundation, Palo Alto, California.
  • Lisa F Newcomb
    Department of Urology, University of Washington Medical Center, Seattle, Washington.
  • Daniel W Lin
    Department of Urology, University of Washington Medical Center, Seattle, Washington.
  • Peter S Nelson
    Department of Medicine, Division of Oncology, University of Washington, Seattle, WA, USA.
  • Ziding Feng
    Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston Texas 77030, U.S.A.
  • Hilary D Boyer
    Program of Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
  • Maria S Tretiakova
    Department of Pathology, University of Washington Medical Center, Seattle, Washington.
  • Larry D True
    Department of Pathology, University of Washington Medical Center, Seattle, Washington.
  • Funda Vakar-Lopez
    Department of Pathology, University of Washington Medical Center, Seattle, Washington.
  • Peter R Carroll
    University of California, San Francisco, San Francisco, CA.
  • Matthew R Cooperberg
    University of California, San Francisco, San Francisco, CA.
  • Emily Chan
    Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California.
  • Jeff Simko
    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.
  • Ladan Fazli
    Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • Martin Gleave
    The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada.
  • Antonio Hurtado-Coll
    The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada.
  • Ian M Thompson
    CHRISTUS Medical Center Hospital, San Antonio, Texas.
  • Dean Troyer
    Department of Pathology, Eastern Virginia Medical School, Norfolk, Virginia; Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia.
  • Jesse K McKenney
    Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Peter L Choyke
    Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Gennady Bratslavsky
  • Baris Turkbey
    Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • D Robert Siemens
    Department of Urology, Queen's University, Kingston, Ontario, Canada.
  • Jeremy Squire
    Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
  • Yingwei P Peng
    Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada; Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada.
  • James D Brooks
    Department of Urology, Stanford School of Medicine, CA.
  • Tamara Jamaspishvili
    Division of Cancer Biology & Genetics, Cancer Research Institute, Queen's University, Kingston, ON, Canada. tamara.jamaspishvili@queensu.ca.

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