Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.

Journal: Thoracic cancer
Published Date: Jan 6, 2022

Abstract

BACKGROUND: Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs.

Authors

  • Zhixin Qiu
    Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
  • Qingxia Wu
    College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China.
  • Shuo Wang
    College of Tea & Food Science, Anhui Agricultural University, Hefei, China.
  • Zhixia Chen
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
  • Feng Lin
    Radiology Department, The People's Hospital of Lezhi, Ziyang, Sichuan, China.
  • Yuyan Zhou
    Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China.
  • Jing Jin
    College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
  • Jinghong Xian
    Department of Clinical Research, West China Hospital, Sichuan University, Chengdu, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Weimin Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.

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