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Published on: April 9, 2019
Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering
Baris Turkbey1, Henkjan Huisman1, Andriy Fedorov1
1From 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.).
This guide outlines requirements for developing artificial intelligence (AI) models to detect prostate cancer (PCa) using MRI. It emphasizes data standards, performance metrics, and transparency for clinical translation.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Oncology
- Prostate Cancer Diagnostics
Background:
- Developing AI for prostate cancer (PCa) detection via MRI requires standardized reporting.
- Biopsy-naive men with positive screening require reliable AI interpretation models.
Purpose of the Study:
- To define key considerations for developing and reporting AI interpretation models for PCa detection in MRI.
- To provide specific data and performance metric requirements for this AI use case.
Main Methods:
- Emphasis on data requirements for transparency and characterization of training/test data.
- Inclusion of true-negative examination definitions (minimum 2-year follow-up), image quality assessments, and nonimaging metadata.
- Performance metrics include 40%-70% cancer detection rate for PI-RADS 4+ lesions and human-level performance benchmarks.
Main Results:
- Provides a checklist for AI in Medical Imaging conformity.
- Encourages the use of open datasets for AI model development.
- Offers guidance based on prostate MRI subspecialty expertise.
Conclusions:
- Accelerates clinical translation of AI in PCa detection.
- Addresses the evolving regulatory landscape for AI in medical diagnostics.
- Establishes benchmarks for AI model development and reporting in prostate MRI.
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