A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Journal: Neuro-oncology
Published Date: Mar 5, 2020

Abstract

BACKGROUND: Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network.

Authors

  • Chandan Ganesh Bangalore Yogananda
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Bhavya R Shah
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Maryam Vejdani-Jahromi
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Sahil S Nalawade
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Gowtham K Murugesan
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Frank F Yu
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Marco C Pinho
  • Benjamin C Wagner
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Bruce Mickey
    Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Toral R Patel
    Department of Neurological Surgery, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Baowei Fei
  • Ananth J Madhuranthakam
    Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Joseph A Maldjian
    Department of Radiology, UT Southwestern Medical Center, Dallas, USA.

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