Lyu SY, Zhang Y, Zhang MW, Zhang BS, Gao LB, Bai LT, Wang J. Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules. World J Clin Cases 2022; 10(2): 518-527 [PMID: 35097077 DOI: 10.12998/wjcc.v10.i2.518]
Corresponding Author of This Article
Yan Zhang, MD, Chief Doctor, Interventional Therapy Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Ningbo 315010, Zhejiang Province, China. wjcclsy@163.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Jan 14, 2022; 10(2): 518-527 Published online Jan 14, 2022. doi: 10.12998/wjcc.v10.i2.518
Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules
Shu-Yi Lyu, Yan Zhang, Mei-Wu Zhang, Bai-Song Zhang, Li-Bo Gao, Lang-Tao Bai, Jue Wang
Shu-Yi Lyu, Yan Zhang, Mei-Wu Zhang, Bai-Song Zhang, Li-Bo Gao, Lang-Tao Bai, Interventional Therapy Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China
Shu-Yi Lyu, Yan Zhang, Mei-Wu Zhang, Bai-Song Zhang, Li-Bo Gao, Lang-Tao Bai, Interventional Therapy Department, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China
Jue Wang, Ultrasonography Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China
Jue Wang, Ultrasonography Department, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China
Author contributions: Lyu SY designed and performed the research and wrote the paper; Zhang Y designed the research and supervised the report; Zhang MW and Zhang BS designed the research and contributed to the analysis; Gao LB and Bai LT collected data and contributed to the analysis; Wang J supervised the report.
Supported byZhejiang Medical and Health Science and Technology Plan Project, No. 2020KY837 and No. 2020KY852.
Institutional review board statement: The study was approved by the ethics committee of Hwa Mei Hospital, University of Chinese Academy of Sciences (Approval No. pj-nbey-ky-2019-060-01).
Informed consent statement: All the subjects signed informed consent before the examination.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
Data sharing statement: Except the data in the text, no other data can be provided.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yan Zhang, MD, Chief Doctor, Interventional Therapy Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Ningbo 315010, Zhejiang Province, China. wjcclsy@163.com
Received: September 14, 2021 Peer-review started: September 14, 2021 First decision: October 18, 2021 Revised: October 22, 2021 Accepted: November 29, 2021 Article in press: November 29, 2021 Published online: January 14, 2022 Processing time: 119 Days and 19.4 Hours
ARTICLE HIGHLIGHTS
Research background
With the popularization of breast screening, an increasing number of BI-RADS 4 nodules have been detected. According to clinical guidelines, such nodules require biopsy. However, the vast majority of BI-RADS 4 nodules are benign, which results in a large number of unnecessary biopsies.
Research motivation
To reduce the biopsy rate for BI-RADS 4 nodules and prevent the waste of medical resources.
Research objectives
Our goal is to improve the preoperative diagnostic accuracy of breast nodules as much as possible, not only to reduce misdiagnosis and missed diagnosis, but also to avoid unnecessary biopsy.
Research methods
We used an artificial intelligence (AI) system to regrade BI-RADS 4 nodules and used pathology results as the gold standard.
Research results
The diagnostic value of AI detection system is higher than that of other methods. The BI-RADS classification results adjusted by AI detection system are closer to the pathological results.
Research conclusions
The AI system has very high diagnostic efficiency for BI-RADS 4 nodules and can effectively prevent many unnecessary puncture biopsies of such nodules.
Research perspectives
In the future, we will continue to study the application of AI in breast cancer and use AI to predict the prognosis of breast cancer.
