Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

Authors

  • Jing Yan
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Qiuchang Sun
    Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Xiangliang Tan
    Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Chaofeng Liang
    Department of Neurosurgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Hongmin Bai
    Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China.
  • Wenchao Duan
    Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Tianhao Mu
    Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Yang Guo
    Innovation Research Institute of Combined Acupuncture and Medicine, Shaanxi University of CM, Xianyang 712046, China.
  • Yuning Qiu
    Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Jian she Dong Road 1, Zhengzhou, 450052, Henan province, China.
  • Weiwei Wang
  • Qiaoli Yao
    Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Dongling Pei
    Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Yuanshen Zhao
    Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Danni Liu
    HaploX Biotechnology, Shenzhen, Guangdong, China.
  • Jingxian Duan
    Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Shifu Chen
    Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Chen Sun
    State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Wenqing Wang
    National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China.
  • Zhen Liu
    School of Pharmacy, Fudan University, PR China; Analytical Service Unit, WuXi AppTec (Shanghai) Co., Ltd, Shanghai, 200131, PR China.
  • Xuanke Hong
    Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Xiangxiang Wang
    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China.
  • Yu Guo
    Animal Disease Control Center of Inner Mongolia, Hohhot, China.
  • Yikai Xu
  • Xianzhi Liu
    Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Jingliang Cheng
    Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Zhi-Cheng Li
    Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. zc.li@siat.ac.cn.
  • Zhenyu Zhang
    Laboratory of Industrial Biotechnology of Department of Education, Jiangnan University, Wuxi 214122, Jiangsu, China.