Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.

Journal: Academic radiology
Published Date:

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.

Authors

  • Chengcheng Xia
    School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.); Jiangxi Provincial Key Laboratory of Disease Prevention and Public Health, Nanchang University, Nanchang 330006, China (C.X., L.D., W.C., M.H.).
  • Minjing Zuo
    Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China.
  • Ze Lin
    Department of Radiology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430022, China (Z.L.); Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan 430022, China (Z.L.).
  • Libin Deng
    Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, P.R. China.
  • Yulian Rao
    Wanli District Center for Disease Control and Prevention of Nanchang, Nanchang 330004, China (Y.R.).
  • Wenxiang Chen
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
  • Jinqin Chen
    Jiangxi Medical College, Nanchang University, Nanchang, China (J.C.).
  • Weirong Yao
    Department of Oncology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China (W.Y.).
  • Min Hu
    Graduate School of Medical Sciences, Kyushu University, Fukuoka City, Fukuoka, Japan.