Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Meningioma consistency critically impacts surgical planning, as soft tumors are easier to resect than hard tumors. Current assessments of tumor consistency using MRI are subjective and lack quantitative accuracy. Integrating deep learning and radiomics could enhance the predictive accuracy of meningioma consistency.

Authors

  • Huanjie Lin
    Department of Radiology, The Second Affiliated Hospital of Guangzhou Medical University, No.250, East Changgang Road, Haizhu District, Guangzhou, 510260, China.
  • Yubiao Yue
    School of Biomedical Engineering, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, 511436, China.
  • Li Xie
    Department of Pharmacy The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Bingbing Chen
    Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
  • Weifeng Li
    Department of Emergency Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Qinrong Zhang
    Guangzhou Medical University, Guangzhou, China.
  • Huai Chen
    Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510120, China.

Keywords

External Resources

Popular Topics
Recent Journals