Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.

Journal: Endocrine
Published Date: Mar 8, 2025

Abstract

BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having varying degrees of malignancy risk, with the risk increasing from TI-RADS 3 to TI-RADS 5. While some of these nodules may undergo fine-needle aspiration (FNA) biopsy to assess their nature, this procedure carries a risk of false negatives and inherent complications. To avoid the need for unnecessary biopsy examination, we explored a method for distinguishing the benign and malignant characteristics of thyroid TI-RADS 3-5 nodules based on deep-learning ultrasound images combined with computed tomography (CT).

Authors

  • Tingting Qian
    Graduate School, The Second Clinical Medical College of Zhejiang Chinese Medical University, Hang Zhou, Zhejiang, 310014, China.
  • Xuhan Feng
    School of Molecular Medicine, Hangzhou institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou, Zhejiang, 310024, People's Republic of China.
  • Yahan Zhou
    Wenling Medical Big Data and Artificial Intelligence Research Institute, Taizhou, China.
  • Shan Ling
    Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
  • Jincao Yao
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Min Lai
    Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China; Taizhou Cancer Hospital, Taizhou, China; Key Laboratory of Minimally Invasive Interventional Therapy and Big Data Artificial Intelligence in Medicine of Taizhou, Taizhou, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Jun Lin
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

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