Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.

Journal: Clinical neuroradiology
Published Date:

Abstract

PURPOSE: The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

Authors

  • Chongfeng Duan
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Fang Liu
    The First Clinical Medical College of Gannan Medical University, Ganzhou 341000, Jiangxi Province, China.
  • Song Gao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Jiping Zhao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Lei Niu
    Department of Emergency, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
  • Nan Li
    School of Basic Medical Sciences, Jiamusi University No. 258, Xuefu Street, Xiangyang District, Jiamusi 154007, Heilongjiang, China.
  • Song Liu
    Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
  • Xiaoming Zhou
    a Department of Pathology, Guangzhou Medical University, Guangzhou, Guangdong Province, China.
  • Yande Ren
    Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong 265000, P.R.China.
  • Wenjian Xu
    Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China.
  • Xuejun Liu
    Department of Radiology, Hospital Affiliated to Qingdao University, Qingdao, China.