Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.

Journal: Frontiers in neurology
Published Date: Jan 1, 2025

Abstract

BACKGROUND: Accurate preoperative grading of meningiomas is crucial for selecting the most suitable treatment strategies and predicting patient outcomes. Traditional MRI-based assessments are often insufficient to distinguish between low- and high-grade meningiomas reliably. Deep learning (DL) models have emerged as promising tools for automated histopathological grading using imaging data. This systematic review and meta-analysis aimed to comprehensively evaluate the diagnostic performance of deep learning (DL) models for meningioma grading.

Authors

  • Parsia Noori Mirtaheri
    School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Matin Akhbari
    Faculty of Medicine, Istanbul Yeni Yuzyil University, Istanbul, Türkiye.
  • Farnaz Najafi
    School of Medicine, Islamic Azad University of Medical Sciences, Tehran, Iran.
  • Hoda Mehrabi
    Student Research Committee, School of Medicine, Arak University of Medical Sciences, Arak, Iran.
  • Ali Babapour
    Department of Computer Science, Tabari Institute of Higher Education, Tehran, Iran.
  • Zahra Rahimian
    School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Amirhossein Rigi
    Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Saeid Rahbarbaghbani
    Faculty of Medicine, Istanbul Yeni Yuzyil University, Istanbul, Türkiye.
  • Hesam Mobaraki
    Faculty of Medicine, İstanbul Yeniyuzyil University, Istanbul, Turkey.
  • Sanaz Masoumi
    Yas Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
  • Danial Nouri
    School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Seyedeh-Tarlan Mirzohreh
    Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
  • Seyyed Kiarash Sadat Rafiei
    Student Research Committee, Shahid Beheshti University of Medical Science, Arabi Ave, Daneshjoo Blvd, Velenjak, Tehran, 19839-63113, Iran.
  • Mahsa Asadi Anar
    Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Zahra Golkar
    Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Yasaman Asadollah Salmanpour
    Student Research Committee, Islamic Azad University Science and Research Branch, Tehran, Iran.
  • Ali Vesali Mahmoud
    Department of Physiology, Buali Sina University, Hamedan, Iran.
  • Mohammad Sadra Gholami Chahkand
    Student Research Committee, School of Medicine, Golestan University of Medical Sciences, Gorgan, Iran.
  • Maryam Khodaei
    Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.

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