Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers.

Journal: Neuro-oncology
Published Date: Jan 30, 2021

Abstract

BACKGROUND: Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification.

Authors

  • Lei Jin
    Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269, United States.
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Qiuping Chun
    Shanghai United Imaging Intelligence Co, Shanghai, China.
  • Hong Chen
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Yixin Ma
    Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China.
  • Shuai Wu
    School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China.
  • N U Farrukh Hameed
    Glioma Surgery Division, Neurologic Surgery Department, Huashan Hospital, Fudan University, Shanghai, China.
  • Chunming Mei
    Wuhan Zhongji Biotechnology Co, Wuhan, China.
  • Junfeng Lu
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Jun Zhang
    First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Abudumijiti Aibaidula
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Jinsong Wu
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.

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