Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis

Zhao Yao, Ting Luo, YiJie Dong, XiaoHong Jia, YinHui Deng, GuoQing Wu, Ying Zhu, JingWen Zhang, Juan Liu, LiChun Yang, XiaoMao Luo, ZhiYao Li, YanJun Xu, Bin Hu, YunXia Huang, Cai Chang, JinFeng Xu, Hui Luo, FaJin Dong, XiaoNa Xia, ChengRong Wu, WenJia Hu, Gang Wu, QiaoYing Li, Qin Chen, WanYue Deng, QiongChao Jiang, YongLin Mou, HuanNan Yan, XiaoJing Xu, HongJu Yan, Ping Zhou, Yang Shao, LiGang Cui, Ping He, LinXue Qian, JinPing Liu, LiYing Shi, YaNan Zhao, YongYuan Xu, WeiWei Zhan, YuanYuan Wang, JinHua Yu () and JianQiao Zhou ()
Additional contact information
Zhao Yao: Fudan University
Ting Luo: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
YiJie Dong: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
XiaoHong Jia: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
YinHui Deng: Fudan University
GuoQing Wu: Fudan University
Ying Zhu: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
JingWen Zhang: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Juan Liu: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
LiChun Yang: Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University
XiaoMao Luo: Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University
ZhiYao Li: Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University
YanJun Xu: Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Institute of Ultrasound in Medicine
Bin Hu: Minhang Hospital, Fudan University
YunXia Huang: Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University
Cai Chang: Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University
JinFeng Xu: Shenzhen People’s Hospital
Hui Luo: Shenzhen People’s Hospital
FaJin Dong: Shenzhen People’s Hospital
XiaoNa Xia: The First Affiliated Hospital of Xi’an Jiaotong University
ChengRong Wu: The First Affiliated Hospital of Xi’an Jiaotong University
WenJia Hu: People’s Hospital of Henan Province
Gang Wu: People’s Hospital of Henan Province
QiaoYing Li: Tangdu Hospital, Four Military Medical University
Qin Chen: Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China
WanYue Deng: Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China
QiongChao Jiang: Sun Yat-sen Memorial Hospital, Sun Yat-sen University
YongLin Mou: General Hospital of Northern Theater Command
HuanNan Yan: General Hospital of Northern Theater Command
XiaoJing Xu: Affiliated Hangzhou First people’s Hospital, Zhejiang University School of Medicine
HongJu Yan: Affiliated Hangzhou First people’s Hospital, Zhejiang University School of Medicine
Ping Zhou: The Third Xiangya Hospital of Central South University
Yang Shao: The Third Xiangya Hospital of Central South University
LiGang Cui: Peking University Third Hospital
Ping He: Peking University Third Hospital
LinXue Qian: Beijing Friendship Hospital, Capital Medical University
JinPing Liu: Beijing Friendship Hospital, Capital Medical University
LiYing Shi: Affiliated Hospital of Guizhou Medical University
YaNan Zhao: Second Affiliated Hospital of Zhejiang University, School of Medicine
YongYuan Xu: Second Affiliated Hospital of Zhejiang University, School of Medicine
WeiWei Zhan: Ruijin Hospital, Shanghai Jiaotong University School of Medicine
YuanYuan Wang: Fudan University
JinHua Yu: Fudan University
JianQiao Zhou: Ruijin Hospital, Shanghai Jiaotong University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Elastography 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.

Date: 2023
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DOI: 10.1038/s41467-023-36102-1

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