Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

Journal: AJNR. American journal of neuroradiology
Published Date: Jul 1, 2018

Abstract

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

Authors

  • P Chang
    From the Department of Radiology (P.C., S.C.), University of California, San Francisco, San Francisco, California.
  • J Grinband
    Department of Radiology (J.G.), Columbia University, New York, New York.
  • B D Weinberg
    Department of Radiology (B.D.W.), Emory University School of Medicine, Atlanta, Georgia.
  • M Bardis
    Departments of Radiology (M.B., M.K., M.-Y.S., D.C.).
  • M Khy
    Departments of Radiology (M.B., M.K., M.-Y.S., D.C.).
  • G Cadena
    Neurosurgery (G.C.).
  • M-Y Su
    Departments of Radiology (M.B., M.K., M.-Y.S., D.C.).
  • S Cha
    From the Department of Radiology (P.C., S.C.), University of California, San Francisco, San Francisco, California.
  • C G Filippi
    Department of Radiology (C.G.F.), North Shore University Hospital, Long Island, New York.
  • D Bota
    Neuro-Oncology (D.B.).
  • P Baldi
    School of Information and Computer Sciences (P.B.), University of California, Irvine, Irvine, California.
  • L M Poisson
    Department of Public Health Sciences (L.M.P.), Henry Ford Health System, Detroit, Michigan.
  • R Jain
    Departments of Radiology and Neurosurgery (R.J.), New York University, New York, New York.
  • D Chow
    Departments of Radiology (M.B., M.K., M.-Y.S., D.C.) chowd3@uci.edu.

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