Thyroid nodules risk stratification through deep learning based on ultrasound images.

Journal: Medical physics
Published Date: Nov 14, 2020

Abstract

PURPOSE: Clinically, the risk stratification of thyroid nodules is usually used to formulate the next treatment plan. The American College of Radiology (ACR) thyroid imaging reporting and data system (TI-RADS) is a type of medical standard widely used in classification diagnosis. It divides the nodule's ACR TI-RADS level into five levels by quantitative scoring, from benign to high suspicion of malignancy. However, such assessment often relies on the radiologists' experience and is time consuming. So computer-aided diagnosis is necessary. But many deep learning (DL) models are difficult for doctors to understand, limiting their applicability in clinical practice. In this work, we mainly focus on how to achieve automatic thyroid nodules risk stratification based on deep integration of deep learning and clinical experience.

Authors

  • Ziyu Bai
    College of Intelligence and Computing, Tianjin University, Tianjin, China.
  • Luchen Chang
    Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
  • Ruiguo Yu
    College of Intelligence and Computing, Tianjin University, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin Key Laboratory of Advanced Networking, Tianjin, China (mainland).
  • Xuewei Li
    Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China.
  • Xi Wei
    Department of Diagnostic and Therapeutic Ultrasonography, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
  • Mei Yu
  • Zhiqiang Liu
    Shenzhen Key Laboratory of Reproductive Immunology for Peri-implantation, Shenzhen Zhongshan Institute for Reproductive Medicine and Genetics, Shenzhen, China.
  • Jie Gao
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Jialin Zhu
    Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China (mainland).
  • Yulin Zhang
  • Shuaijie Wang
    Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China.
  • Zhuo Zhang

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