Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China.

Journal: Frontiers in immunology
Published Date:

Abstract

OBJECTIVE: To develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early in patients with adult anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in Southwest China.

Authors

  • Yayun Xiang
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xiaoxuan Dong
    College of Computer and Information Science, Chongqing, China.
  • Chun Zeng
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Junhang Liu
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Hanjing Liu
    Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China.
  • Xiaofei Hu
    Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
  • Jinzhou Feng
    Department of Neurology, First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Silin Du
    Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China.
  • Jingjie Wang
    Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China.
  • Yongliang Han
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Qi Luo
    B-DAT & CICAEET, School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, PR China.
  • Shanxiong Chen
  • Yongmei Li
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.