A cross-modal 3D deep learning for accurate lymph node metastasis prediction in clinical stage T1 lung adenocarcinoma.

Journal: Lung cancer (Amsterdam, Netherlands)
Published Date:

Abstract

OBJECTIVES: The evaluation of lymph node (LN) status by radiologists based on preoperative computed tomography (CT) lacks high precision for early lung cancer patients; erroneous evaluations result in inappropriate therapeutic plans and increase the risk of complications. This study aims to develop a cross-modal 3D neural network based on CT images and prior clinical knowledge for accurate prediction of LN metastasis in clinical stage T1 lung adenocarcinoma.

Authors

  • Xingyu Zhao
    University of Science and Technology of China, Hefei, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Xiang Wang
    Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Wei Xia
    Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Qiong Li
    Department of Burns & Wound Care Centre, 2nd Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310000, Zhejiang Province, China. 2504131@zju.edu.cn.
  • Liu Zhou
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Qingchu Li
    Department of Radiology, Changzheng Hospital of the Navy Medical University, Shanghai, China.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Jiali Cai
    Department of Radiology, Changzheng Hospital of the Navy Medical University, Shanghai, China.
  • Junming Jian
    University of Science and Technology of China, Hefei, Anhui, 230026, China.
  • Li Fan
    Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Honglin Bai
    University of Science and Technology of China, Hefei, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Zhen Li
    PepsiCo R&D, Valhalla, NY, United States.
  • Yi Xiao
    Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
  • Yuguo Tang
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Xin Gao
    Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
  • Shiyuan Liu
    Department of Radiology, Changzheng Hospital of the Navy Medical University, Shanghai, China. Electronic address: liushiyuan@smmu.edu.cn.