Performance of Machine Learning Models in Predicting BRAF Alterations Using Imaging Data in Low-Grade Glioma: A Systematic Review and Meta-Analysis.

Journal: World neurosurgery
PMID: 39914655

Abstract

BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted in effective predictive models. Therefore, for the first time, this study aimed to review the performance of machine learning and deep learning models in predicting the BRAF alterations in low-grade gliomas (LGGs)using imaging data.

Authors

  • Shahryar Rajai Firouzabadi
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
  • Roozbeh Tavanaei
    Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran.
  • Ida Mohammadi
    Farzan Artificial Intelligence Team, Farzan Clinical Research Institute, Tehran, Islamic Republic of Iran.
  • Alireza Alikhani
  • Ali Ansari
    Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Mohammadhosein Akhlaghpasand
    Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences (IUMS), Tehran, 14665-354, Iran. Akhlaghpasandm@yahoo.com.
  • Bardia Hajikarimloo
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Raymund L Yong
    Department of Neurosurgery, Mount Sinai Hospital, Icahn School of Medicine, New York City, NY, USA.
  • Konstantinos Margetis
    Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.

Keywords

External Resources

Popular Topics
Recent Journals