Age-stratified deep learning model for thyroid tumor classification: a multicenter diagnostic study.

Journal: European radiology
Published Date: Feb 4, 2025

Abstract

OBJECTIVES: Thyroid cancer, the only cancer that uses age as a specific predictor of survival, is increasing in incidence, yet it has a low mortality rate, which can lead to overdiagnosis and overtreatment. We developed an age-stratified deep learning (DL) model (hereafter, ASMCNet) for classifying thyroid nodules and aimed to investigate the effect of age stratification on the accuracy of a DL model, exploring how ASMCNet can help radiologists improve diagnostic performance and avoid unnecessary biopsies.

Authors

  • Weijie Zou
    Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, China.
  • Yahan Zhou
    Wenling Medical Big Data and Artificial Intelligence Research Institute, Taizhou, China.
  • Jincao Yao
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Bojian Feng
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Danlei Xiong
    Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • 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.
  • Lingyan Zhou
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China.
  • Liping Wang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200011, China.
  • Liyu Chen
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Ping Liang
    Department of Pharmacy, The Fourth Hospital of Hebei Medical University Shijiazhuang 050000, Hebei, China.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

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