Prostate imaging reporting and data system Version 2.1: 2019 update of prostate imaging reporting and data system version 2
Abstract
The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was developed with a consensus-based process using a combination of published data, and expert observations and opinions. In the short time since its release, numerous studies have validated the value of PI-RADS v2 but, as expected, have also identified a number of ambiguities and limitations, some of which have been documented in the literature with potential solutions offered. To address these issues, the PI-RADS Steering Committee, again using a consensus-based process, has recommended several modifications to PI-RADS v2, maintaining the framework of assigning scores to individual sequences and using these scores to derive an overall assessment category. This updated version, described in this article, is termed PI-RADS v2.1. It is anticipated that the adoption of these PI-RADS v2.1 modifications will improve inter-reader variability and simplify PI-RADS assessment of prostate magnetic resonance imaging even further. Research on the value and limitations on all components of PI-RADS v2.1 is strongly encouraged.
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Hamm and colleagues presented an explainable AI model that outputs predictions in terms of PI-RADS criteria.
“Hamm and colleagues (38) presented an explainable AI model that goes beyond classification to output AI predictions in terms of PI-RADS criteria (39)”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [39]
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Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
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Interactive explainable deep learning model informs prostate cancer diagnosis at MRI
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- Authors
- Turkbey, B., Rosenkrantz, A. B., Haider, M. A., Padhani, A. R., Villeirs, G., Macura, K. J., Tempany, C. M., Choyke, P. L., Cornud, F., Margolis, D. J., Thoeny, H. C., Verma, S., Barentsz, J., & Weinreb, J. C.
- Affiliations
- NYU Langone Health (US)
- University of Toronto (CA)
- Mount Vernon Cancer Centre (GB)
- Ghent University Hospital (BE)
- Johns Hopkins University (US)
- Brigham and Women's Hospital (US)
- National Institutes of Health (US)
- Cornell University (US)
- University of Fribourg (CH)
- University of Cincinnati (US)
- Radboud University Medical Center (NL)
- Yale University (US)
- Corresponding author
- Rosenkrantz, Andrew B.
- ORCID
- Venue
- European Urology
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 76
- Issue
- 3
- Pages
- 340-351
- ISSN
- 0302-2838 (ISSN-L)
- OpenAlex
- W2922071185
- Cited by
- 2,544
- References
- 35
- Citations by year
- 2026: 378 · 2025: 443 · 2024: 421 · 2023: 389 · 2022: 308 · 2021: 289 · 2020: 230 · 2019: 75 · 2018: 5 · 2017: 1 · 2016: 1 · 2015: 1
- FWCI
- 38.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Urology (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- PI-RADS v2.1, prostate MRI, prostate cancer, reporting and data system, inter-reader variability, prostate imaging
- MeSH terms
- Data Systems, Humans, Magnetic Resonance Imaging, Male, Prostate, Prostatic Neoplasms, Consensus, Image-Guided Biopsy
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