A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas.

Journal: NeuroImage. Clinical
Published Date:

Abstract

OBJECTIVES: To investigate the association between proton magnetic resonance spectroscopy (H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade.

Authors

  • Chong Qi
    Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Xing Fan
    Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
  • Yin Jiang
    Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Song Yang
    Key Laboratory of Pesticide Toxicology&Application Technique, College of Plant Protection, Shandong Agricultural University, Tai'an 271018, China.
  • Lanxi Meng
    Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
  • Tao Jiang
    Department of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
  • Shaowu Li
    Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.