Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.

Journal: Journal of neuro-oncology
Published Date: May 16, 2018

Abstract

INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a glioma. The aim of this study is to devise a machine learning algorithm that may be used by radiologists in routine practice to aid diagnosis of both: WHO grade and IDH mutation status in de novo gliomas.

Authors

  • Céline De Looze
    Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.
  • Alan Beausang
    Department of Neuropathology, Beaumont Hospital, Dublin, Ireland.
  • Jane Cryan
    Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.
  • Teresa Loftus
    Department of Molecular Pathology, Beaumont Hospital, Dublin, Ireland.
  • Patrick G Buckley
    Department of Molecular Pathology, Beaumont Hospital, Dublin, Ireland.
  • Michael Farrell
    Department of Neuropathology, Beaumont Hospital, Dublin, Ireland.
  • Seamus Looby
    Neurointerventional Service, Department of Radiology, Beaumont Hospital, Dublin, Ireland.
  • Richard Reilly
    Trinity Centre for Bioengineering, Trinity College Dublin, Dublin, Ireland.
  • Francesca Brett
    Department of Neuropathology, Beaumont Hospital, Dublin, Ireland.
  • Hugh Kearney
    Department of Neuropathology, Beaumont Hospital, Dublin, Ireland. hugh.kearney.10@ucl.ac.uk.

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