The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis.

Journal: World neurosurgery
PMID: 37995996

Abstract

BACKGROUND: Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27 M mutation carry a poor prognosis. In recent years, machine learning (ML)-based radiomics have shown promising results in predicting gene mutation status non-invasively. Therefore, this study aims to comprehensively evaluate the diagnostic performance of ML-based magnetic resonance imaging radiomics in predicting H3K27 M mutation status in DMG patients.

Authors

  • Mohammad Amin Habibi
    Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Fateme Aghaei
    Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran.
  • Zohreh Tajabadi
    Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.
  • Mohammad Sina Mirjani
    Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran.
  • Poriya Minaee
    Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran.
  • SeyedMohammad Eazi
    Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran.

Keywords

External Resources

Popular Topics
Recent Journals