Whole-lesion-aware network based on freehand ultrasound video for breast cancer assessment: a prospective multicenter study.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date:

Abstract

BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast lesions on static images. To better exploit the real-time advantages of ultrasound and more conducive to clinical application, we proposed a whole-lesion-aware network based on freehand ultrasound video (WAUVE) scanning in an arbitrary direction for predicting overall breast cancer risk score.

Authors

  • Jie Han
    Department of Neurology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
  • Yuanjing Gao
    Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Shuaifuyuan 1St, Beijing, 100730, China.
  • Ling Huo
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Breast Center, Peking University Cancer Hospital & Institute, Beijing, China.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Xiaozheng Xie
    State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Mengsu Xiao
    Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Shuaifuyuan 1St, Beijing, 100730, China.
  • Nan Zhang
    Department of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, China.
  • Meng Lei
    Center for Data Science, Peking University, Beijing, China.
  • Quanlin Wu
    Center for Data Science, Peking University, Beijing, China.
  • Lu Ma
    State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Chao Sun
    Hospital for Skin Diseases and Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, China.
  • Xinyi Wang
    School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.
  • Lei Liu
    Department of Science and Technology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Shuzhen Cheng
    Department of Ultrasound, Nanchang People's Hospital, Nanchang, China.
  • Binghui Tang
    Department of Psychological & Brain Sciences, Neuroscience Program, Colgate University, Hamilton, NY, USA.
  • Liwei Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Qingli Zhu
    Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Shuaifuyuan 1St, Beijing, 100730, China. zqlpumch@126.com.
  • Yong Wang
    State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University Hunghom Kowloon Hong Kong P. R. China kwok-yin.wong@polyu.edu.hk.