Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study.

Journal: BMC medicine
Published Date:

Abstract

BACKGROUND: Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses.

Authors

  • Bin Long
    Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843, USA.
  • Haoyan Zhang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
  • Han Zhang
    Johns Hopkins University, Baltimore, MD, USA.
  • Wen Chen
    School of Cyber Science and Engineering, Sichuan University, Chengdu, Sichuan, China.
  • Yang Sun
    Department of Gastroenterology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Rui Tang
    State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
  • Yuxuan Lin
    Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Qiang Fu
    Department of Sociology, The University of British Columbia, Vancouver, British Columbia, Canada, V6 T 1Z1.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Ligang Cui
    Department of Ultrasound, Peking University Third Hospital, Beijing, China. Electronic address: cuiligang_bysy@126.com.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.