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.
Radiology. 2025 Apr [Epub]
Baris Turkbey, Henkjan Huisman, Andriy Fedorov, Katarzyna J Macura, Daniel J Margolis, Valeria Panebianco, Aytekin Oto, Ivo G Schoots, M Minhaj Siddiqui, Caroline M Moore, Olivier Rouvière, Leonardo K Bittencourt, Anwar R Padhani, Clare M Tempany, Masoom A Haider
From the Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Room B3B85, Bethesda, MD 20892 (B.T.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (H.H.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.F., C.M.T.); The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (K.J.M.); Department of Radiology, Weill Cornell Medicine/New York Presbyterian, New York, NY (D.J.M.); Department of Radiological Sciences, Oncology and Pathology, Sapienza University, Rome, Italy (V.P.); Department of Radiology, University of Chicago, Chicago, Ill (A.O.); Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S.); Department of Surgery, Division of Urology, University of Maryland School of Medicine, Baltimore, Md (M.M.S.); Division of Surgery Interventional Science, University College London, London, UK (C.M.M.); Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK (C.M.M.); Department of Urinary and Vascular Imaging, Hospices Civils de Lyon, Hôpital Edouard Herriot, Lyon, France (O.R.); Faculté de Médecine Lyon Est, Université de Lyon, Université Lyon 1, Lyon, France (O.R.); Department of Radiology, University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, Cleveland, Ohio (L.K.B.); Paul Strickland Scanner Centre, Mount Vernon Hospital, Middlesex, UK (A.R.P.); Joint Department of Medical Imaging, Mount Sinai Hospital, Princess Margaret Hospital, University of Toronto, Toronto, Canada (M.A.H.); and Lunenfeld-Tanenbaum Research Institute, Toronto, Canada (M.A.H.).