Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis
-
Published:2023-02-11
Issue:1
Volume:14
Page:
-
ISSN:2041-1723
-
Container-title:Nature Communications
-
language:en
-
Short-container-title:Nat Commun
Author:
Yao Zhao, Luo Ting, Dong YiJie, Jia XiaoHong, Deng YinHuiORCID, Wu GuoQing, Zhu Ying, Zhang JingWen, Liu Juan, Yang LiChun, Luo XiaoMao, Li ZhiYao, Xu YanJun, Hu Bin, Huang YunXia, Chang Cai, Xu JinFeng, Luo Hui, Dong FaJinORCID, Xia XiaoNa, Wu ChengRong, Hu WenJia, Wu Gang, Li QiaoYing, Chen Qin, Deng WanYue, Jiang QiongChao, Mou YongLin, Yan HuanNan, Xu XiaoJing, Yan HongJu, Zhou Ping, Shao Yang, Cui LiGang, He Ping, Qian LinXue, Liu JinPing, Shi LiYing, Zhao YaNan, Xu YongYuan, Zhan WeiWei, Wang YuanYuanORCID, Yu JinHuaORCID, Zhou JianQiaoORCID
Abstract
AbstractElastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.
Funder
National Natural Science Foundation of China Shanghai Science and Technology Development Foundation
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference31 articles.
1. Shen, Y. Q. et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat. Commun. 12, 5645 (2021). 2. Zheng, X. Y. et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat. Commun. 11, 1236 (2020). 3. Clevert, A. et al. ESR statement on portable ultrasound devices. Insights Imaging 10, 89 (2019). 4. Bennett, D. et al. Portable pocket-sized ultrasound scanner for the evaluation of lung involvement in coronavirus disease 2019 patients. Ultrasound Med. Biol. 47, 19–24 (2021). 5. Rykkje, A., Carlsen, J. F. & Nielsen, M. B. Hand-held ultrasound devices compared with high-end ultrasound systems: a systematic review. Diagnostics 9. https://doi.org/10.3390/diagnostics9020061 (2019).
Cited by
21 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
|
|