Deep learning-assisted distinguishing breast phyllodes tumours from fibroadenomas based on ultrasound images: a diagnostic study.

Journal: The British journal of radiology
PMID:

Abstract

OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.

Authors

  • Yuqi Yan
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Yuanzhen Liu
    Department of Diagnostic Ultrasound Imaging and Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, China; Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang 317502, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310018, China. Electronic address: yuanzhen_0128@aliyun.com.
  • Jincao Yao
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Lin Sui
    Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310022, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Tian Jiang
  • Xiaofang Liu
    Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
  • Yifan Wang
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Di Ou
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Lina Feng
    Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Qianmeng Pan
    Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China.
  • Ying Su
    College of Marine Life Science, Ocean University of China, Qingdao, China.
  • Yukai Wang
    Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China.
  • Liping Wang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200011, China.
  • Lingyan Zhou
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.