Development and Validation of a Risk Stratification Model of Pulmonary Ground-Glass Nodules Based on Complementary Lung-RADS 1.1 and Deep Learning Scores.

Journal: Frontiers in public health
Published Date: May 23, 2022

Abstract

PURPOSE: To assess the value of novel deep learning (DL) scores combined with complementary lung imaging reporting and data system 1.1 (cLung-RADS 1.1) in managing the risk stratification of ground-glass nodules (GGNs) and therefore improving the efficiency of lung cancer (LC) screening in China.

Authors

  • Qingcheng Meng
    Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.
  • Bing Li
  • Pengrui Gao
    Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.
  • Wentao Liu
    Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.
  • Peijin Zhou
    Department of Radiology, The People's Hospital of Nanzhao Country, Nanyang, China.
  • Jia Ding
    Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China.
  • Jiaqi Zhang
  • Hong Ge
    Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.

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