A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
Published Date:

Abstract

PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images.

Authors

  • Dan Cheng
    Massachusetts General Hospital, Boston, MA.
  • Zhizheng Zhuo
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Jiang Du
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Jinyuan Weng
    Department of Medical Imaging Product, Neusoft, Group Ltd., Shenyang, People's Republic of China.
  • Chengzhou Zhang
    Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, P.R. China.
  • Yunyun Duan
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Ting Sun
    Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine, School of Biological Science and Medical Engineering, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing, 100083, People's Republic of China.
  • Minghao Wu
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Min Guo
    Key Laboratory of Biology and Sustainable Management of Plant Diseases and Pests of Anhui Higher Education Institutes, Hefei, People's Republic of China.
  • Tiantian Hua
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
  • Ying Jin
    Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China.
  • Boyang Peng
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, P.R. China.
  • Zhaohui Li
    School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei 430070, China.
  • Mingwang Zhu
    Department of Radiology, Sanbo Brain Hospital, Capital Medical University, Beijing, P.R. China.
  • Maliha Imami
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Chetan Bettegowda
    Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Haris Sair
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Frederik Barkhof
    MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands.
  • Xing Liu
    School of Food Science and Engineering, Hainan University 58 Renmin Avenue Haikou 570228 China zhangzeling@hainanu.edu.cn benchao312@hainanu.edu.cn xuhuan.hnu@foxmail.com qichen@hainanu.edu.cn sunzhichang11@163.com hmcao@hainanu.edu.cn.
  • Yaou Liu
    Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, PR China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, PR China.