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, Liang Wang Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital , Beijing , China Search for other works by this author on: Oxford Academic Daniel J. Margolis Department of Radiology, Weill Cornell Medicine/ New York Presbyterian , New York , United States Search for other works by this author on: Oxford Academic Min Chen Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China Search for other works by this author on: Oxford Academic Xinming Zhao Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing , China Search for other works by this author on: Oxford Academic Qiubai Li Department of Radiology, University of Iowa, Roy Carver College of Medicine , Iowa , United States Search for other works by this author on: Oxford Academic Zhenghan Yang Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital , Beijing , China Search for other works by this author on: Oxford Academic Jie Tian Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University , Beijing , China CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences , Beijing , China Search for other works by this author on: Oxford Academic Zhenchang Wang Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital , Beijing , China Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0003-3141-2609 British Journal of Radiology, Volume 95, Issue 1131, 1 March 2022, 20210816, https://doi.org/10.1259/bjr.20210816
Published:
04 February 2022
Article history
Received:
06 July 2021
Revision received:
31 December 2021
Accepted:
10 January 2022
Published:
04 February 2022
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Liang Wang, Daniel J. Margolis, Min Chen, Xinming Zhao, Qiubai Li, Zhenghan Yang, Jie Tian, Zhenchang Wang, Quality in MR reporting of the prostate – improving acquisition, the role of AI and future perspectives, British Journal of Radiology, Volume 95, Issue 1131, 1 March 2022, 20210816, https://doi.org/10.1259/bjr.20210816
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The high quality of MRI reporting of the prostate is the most critical component of the service provided by a radiologist. Prostate MRI structured reporting with PI-RADS v. 2.1 has been proven to improve consistency, quality, guideline-based care in the management of prostate cancer. There is room for improved accuracy of prostate mpMRI reporting, particularly as PI-RADS core criteria are subjective for radiologists. The application of artificial intelligence may support radiologists in interpreting MRI scans. This review addresses the quality of prostate multiparametric MRI (mpMRI) structured reporting (include improvements in acquisition using artificial intelligence) in terms of size of prostate gland, imaging quality, lesion location, lesion size, TNM staging, sector map, and discusses the future prospects of quality in MR reporting.
© 2022 The Authors. Published by the British Institute of Radiology
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
Subject
Genitourinary
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