Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

Authors

  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Di Dong
    The Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Mengjie Fang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Hailiang Li
    Department of Minimally Invasive Intervention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, ZhengZhou, 450008, China.
  • Jianbo Gao
    Department of Radiology, the First Affiliated Hospital of Zhengzhou University, No. 1, East Jianshe Road, Zhengzhou, 450052, Henan, China. gaojianbo_cancer@163.com.