Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.

Authors

  • Ziyang Liu
    Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.
  • Sikang Ren
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Heng Zhang
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhiyi Liao
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Zhiming Liu
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Xu An
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Jian Cheng
  • Chunde Li
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Jian Gong
    Estuarine and Coastal Environment Research Center, Chinese Research Academy of Environmental Sciences, Beijing, 100012, P. R. China.
  • Haijun Niu
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Jing Jing
  • Zixiao Li
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China. lizixiao2008@hotmail.com.
  • Tao Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Yongji Tian
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China. tianyongji@bjtth.org.