Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0.

Journal: Radiology
PMID: 40232134

Abstract

This document defines the key considerations for developing and reporting an artificial intelligence (AI) interpretation model for the detection of clinically significant prostate cancer (PCa) at MRI in biopsy-naive men with a positive clinical screening status. Specific data and performance metric requirements and a checklist are provided for this use case. Data requirements emphasize the need for sufficient information to provide transparency and characterization of training and test data. The definition of a true-negative examination (which includes a minimum of 2-year follow-up), the need for image quality assessments, and nonimaging metadata requirements are provided. Performance metrics ranges are included, such as a cancer detection rate of 40%-70% for Prostate Imaging Reporting and Data System, or PI-RADS, 4 or higher lesions and demonstration of equivalent or better than human performance using receiver operating characteristic and precision-recall curves. The use of open datasets such as those used in the AI challenge model is encouraged. The study design should include conformity with the Checklist for Artificial Intelligence in Medical Imaging requirements. This article should be taken in the context of the current and evolving regulatory landscape. This review provides guidance based on subspeciality expertise in prostate MRI and will hopefully accelerate the clinical translation of AI in PCa detection.

Authors

  • Baris Turkbey
    Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Henkjan Huisman
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Andriy Fedorov
    Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Katarzyna J Macura
    The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
  • Daniel J Margolis
    Weill Cornell Imaging, Cornell University, New York, NY.
  • Valeria Panebianco
    Department of Radiological Sciences, Oncology and Pathology, Sapienza/Policlinico Umberto I, Rome, Italy.
  • Aytekin Oto
    Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, USA.
  • Ivo G Schoots
    Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • M Minhaj Siddiqui
    University of Maryland, Baltimore, MD, USA.
  • Caroline M Moore
    Division of Surgery and Interventional Science, University College London, London, UK.
  • Olivier Rouviere
    Hospices Civils de Lyon, Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Lyon, France.
  • Leonardo K Bittencourt
    University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA (T.F., V.V., V.K., R.B., L.K.B., N.F.).
  • Anwar R Padhani
    From the Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Rickmansworth Road, Northwood, Middlesex HA6 2RN, England (A.R.P.); and Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.).
  • Clare M Tempany
  • Masoom A Haider
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

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