Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.

Journal: Japanese journal of radiology
Published Date: Feb 1, 2020

Abstract

PURPOSE: To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG).

Authors

  • Deniz Alis
    Acıbadem Mehmet Ali Aydınlar University Faculty of Medicine, Department of Radiology, İstanbul, Türkiye.
  • Omer Bagcilar
    Department of Radiology, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, KMPasa, Istanbul, Turkey.
  • Yeseren Deniz Senli
    Department of Radiology, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, KMPasa, Istanbul, Turkey.
  • Mert Yergin
    Department of Software Engineering and Applied Sciences, Bahcesehir University, Istanbul, Turkey.
  • Cihan Isler
    Department of Neurosurgery, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, KMPasa, Istanbul, Turkey.
  • Naci Kocer
    Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Civan Islak
    Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Osman Kizilkilic
    Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey.

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