Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study.

Journal: Annals of oncology : official journal of the European Society for Medical Oncology
Published Date:

Abstract

BACKGROUND: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough.

Authors

  • D Dong
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • M-J Fang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • L Tang
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, China.
  • X-H Shan
    Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
  • J-B Gao
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • F Giganti
    Department of Radiology, University College London Hospital NHS Foundation Trust, London; Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London, UK; Department of Radiology, Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy.
  • R-P Wang
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
  • X Chen
    Division of Infectious Diseases,The People's Hospital of Meizhou,Meizhou,China.
  • X-X Wang
    Department of Radiology, Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
  • D Palumbo
    Department of Radiology, Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
  • J Fu
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, China.
  • W-C Li
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
  • J Li
    Department of Pulmonary and Critical Care Medicine, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
  • L-Z Zhong
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • F De Cobelli
    Department of Radiology, Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
  • J-F Ji
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing, China. Electronic address: jijiafu@hsc.pku.edu.cn.
  • Z-Y Liu
    Department of Radiology, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China. Electronic address: zyliu@163.com.
  • J Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China. Electronic address: jie.tian@ia.ac.cn.