Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: Aug 4, 2025

Abstract

PURPOSE: Accurate preoperative grading of gliomas is critical for therapeutic planning and prognostic evaluation. We developed a noninvasive machine learning model leveraging whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) biomarkers to discriminate high-grade (HGGs) and low-grade gliomas (LGGs) in the frontal lobe.

Authors

  • Yue Hu
    Department of Biobank, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Xin Cao
    Zhongshan Hospital, Institute of Clinical Science, Shanghai Medical College, Fudan University, Shanghai 200032, China.
  • Hongyi Chen
    Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
  • Daoying Geng
    Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai, 200040, China. GengdaoyingGDY@163.com.
  • Kun Lv
    Departments of1Radiology and.

Keywords

External Resources

Popular Topics
Recent Journals