Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.

Journal: European radiology
PMID: 31691122

Abstract

OBJECTIVE: To evaluate the potential value of the machine learning (ML)-based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG), using various state-of-the-art ML algorithms.

Authors

  • Burak Kocak
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey. drburakkocak@gmail.com.
  • Emine Sebnem Durmaz
    Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Ece Ates
    1 Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.
  • Ipek Sel
    Department of Radiology, Istanbul Training and Research Hospital, Samatya, 34098, Istanbul, Turkey.
  • Saime Turgut Gunes
    Department of Radiology, Istanbul Training and Research Hospital, Samatya, 34098, Istanbul, Turkey.
  • Ozlem Korkmaz Kaya
    Department of Radiology, Koc University School of Medicine, Koc University Hospital, Istanbul, Turkey.
  • Amalya Zeynalova
    Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Ozgur Kilickesmez
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.

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