A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

Journal: European radiology experimental
Published Date: Jan 2, 2024

Abstract

BACKGROUND: To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation (21L858R) simultaneously.

Authors

  • Ye Li
    Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Science, Haikou 571010, People's Republic of China; Key Laboratory of Monitoring and Control of Tropical Agricultural and Forest Invasive Alien Pests, Ministry of Agriculture, Haikou 571010, People's Republic of China.
  • Xinna Lv
    Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
  • Cancan Chen
    China Mobile Research Institute, Beijing, 100053, China. Electronic address: chencancan@chinamobile.com.
  • Ruize Yu
    Infervision Medical Technology Co., Ltd., Ocean International Center, Beijing, China.
  • Bing Wang
    Computer Science & Engineering Department at the University of Connecticut.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.
  • Dailun Hou
    Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China. hou.dl@mail.ccmu.edu.cn.

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