Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

Journal: The British journal of radiology
Published Date: Aug 13, 2018

Abstract

OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sensitive mutations of the epidermal growth factor receptor (EGFR) gene. We constructed three-dimensional convolutional neural networks (CNN) to analyze underlying patterns in CT images that could indicate that EGFR gene mutation status but are invisible to human eyes.

Authors

  • Jun-Feng Xiong
  • Tian-Ying Jia
    2 Department of Radiotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai , China.
  • Xiao-Yang Li
    2 Department of Radiotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai , China.
  • Wen Yu
    2 Department of Radiotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai , China.
  • Zhi-Yong Xu
    2 Department of Radiotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai , China.
  • Xu-Wei Cai
    2 Department of Radiotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai , China.
  • Ling Fu
  • Jie Zhang
    College of Physical Education and Health, Linyi University, Linyi, Shandong, China.
  • Bin-Jie Qin
    1 Department of Biomedical Engineering, School of Biomedical Engineering, Shanghai Jiao Tong University , Shanghai , China.
  • Xiao-Long Fu
    2 Department of Radiotherapy, Shanghai Chest Hospital, Shanghai Jiao Tong University , Shanghai , China.
  • Jun Zhao

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