A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

Journal: European radiology
PMID: 37436509

Abstract

OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC).

Authors

  • Juan Wang
    Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Caixia Dong
    Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
  • Yao-Zhong Zhang
    The Institute of Medical Science, The University of Tokyo, Shirokanedai 4-6-1, Minato-ku, Tokyo, 108-8639, Japan.
  • Lirong Wang
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA. Electronic address: liw30@pitt.edu.
  • Xin Yuan
  • Meiqing He
    Department of Ultrasound, Shaanxi Provincial People's Hospital, Xi'an, 710068, China.
  • Songhua Xu
    Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China. Electronic address: songhua_xu1@163.com.
  • Qi Zhou
  • Jue Jiang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.

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