Quality in MR reporting (include improvements in acquisition using AI)-Reference-Cited by-同舟云学术

Quality in MR reporting (include improvements in acquisition using AI)

Published:2022-03-01 Issue:1131 Volume:95 Page:
ISSN:0007-1285
Container-title:The British Journal of Radiology
language:en
Short-container-title:BJR

Author:

Wang Liang¹^ORCID,Margolis Daniel J.²,Chen Min³,Zhao Xinming⁴,Li Qiubai⁵,Yang Zhenghan¹,Tian Jie⁶⁷,Wang Zhenchang¹

Affiliation:

1. Department of Radiology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China

2. Department of Radiology, Weill Cornell Medicine/ New York Presbyterian, New York, United States

3. Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China

4. 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

5. Department of Radiology, University of Iowa, Roy Carver College of Medicine, Iowa, United States

6. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China

7. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Abstract

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.

Publisher

British Institute of Radiology

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://www.birpublications.org/doi/pdf/10.1259/bjr.20210816

Reference33 articles.

1. Prostate metastatic bone cancer in an Egyptian Ptolemaic mummy, a proposed radiological diagnosis

2. Cancer Statistics, 2021

3. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

4. Evidence-Based Reporting: A Method to Optimize Prostate MRI Communications With Referring Physicians

5. Prostatic carcinoma: staging by clinical assessment, CT, and MR imaging.

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Feasibility Study of AI-Assisted Compressed Sensing in Prostate T2-Weighted Imaging;Academic Radiology;2024-07

2. The importance and future of prostate MRI report templates: improving oncological care;Abdominal Radiology;2024-06-20

3. Artificial Intelligence Applications in Prostate Cancer Management: Success Stories and Future Ahead;UroCancer Clinics of India;2024-01

4. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges;European Journal of Radiology;2023-08

5. Innovations in prostate cancer: introductory editorial;The British Journal of Radiology;2022-03