Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics for Prediction of H3K27M Mutation in Midline Gliomas.

Journal: World neurosurgery
Published Date: Jul 1, 2021

Abstract

OBJECTIVE: H3K27M mutation in gliomas has prognostic implications. Previous magnetic resonance imaging (MRI) studies have reported variable rates of tumoral enhancement, necrotic changes, and peritumoral edema in H3K27M-mutant gliomas, with no distinguishing imaging features compared with wild-type gliomas. We aimed to construct an MRI machine learning (ML)-based radiomic model to predict H3K27M mutation in midline gliomas.

Authors

  • Sedat Giray Kandemirli
    Department of Radiology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA. Electronic address: sedat-kandemirli@uiowa.edu.
  • Burak Kocak
    Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey. drburakkocak@gmail.com.
  • Shotaro Naganawa
    Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
  • Kerem Ozturk
    Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
  • Stephen S F Yip
    Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA; AIQ Solutions, Madison, Wisconsin, USA.
  • Saurav Chopra
    Department of Pathology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA.
  • Luciano Rivetti
    FUESMEN and FADESA, Mendoza, Argentina.
  • Amro Saad Aldine
    Department of Radiology, Louisiana State University Health Sciences Center, Louisiana, Missouri, USA.
  • Karra Jones
    Department of Pathology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA.
  • Zuzan Cayci
    Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
  • Toshio Moritani
    Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
  • Takashi Shawn Sato
    Department of Radiology, University of Iowa Hospital and Clinics, Iowa City, Iowa, USA.

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