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Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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, 2023. "Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36102-1
    DOI: 10.1038/s41467-023-36102-1
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    References listed on IDEAS

    as
    1. Xueyi Zheng & Zhao Yao & Yini Huang & Yanyan Yu & Yun Wang & Yubo Liu & Rushuang Mao & Fei Li & Yang Xiao & Yuanyuan Wang & Yixin Hu & Jinhua Yu & Jianhua Zhou, 2020. "Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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