The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas.

Journal: Clinical radiology
Published Date: Jan 21, 2020

Abstract

AIM: To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG).

Authors

  • D Alis
    Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Radiology, Halkali/Istanbul, Turkey. Electronic address: drdenizalis@gmail.com.
  • O Bagcilar
    Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, KMPasa, Istanbul, Turkey.
  • Y D Senli
    Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, KMPasa, Istanbul, Turkey.
  • C Isler
    Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Neurosurgery, KMPasa, Istanbul, Turkey.
  • M Yergin
    Bahcesehir University, Department of Software Engineering and applied sciences, Istanbul, Turkey.
  • N Kocer
    Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, KMPasa, Istanbul, Turkey.
  • C Islak
    Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, KMPasa, Istanbul, Turkey.
  • O Kizilkilic
    Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Radiology, KMPasa, Istanbul, Turkey.

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