Improved Glioma Grading Using Deep Convolutional Neural Networks.

Journal: AJNR. American journal of neuroradiology
Published Date: Dec 10, 2020

Abstract

BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning. The criterion standard for glioma grading is invasive tissue sampling. Recently, radiomic features have shown excellent potential in glioma-grade prediction. These features may not fully exploit the underlying information in MR images. The objective of this study was to investigate the performance of features learned by a convolutional neural network compared with standard radiomic features for grade prediction.

Authors

  • S Gutta
    From the Ming Hsieh Department of Electrical and Computer Engineering (S.G., K.S.N.), Viterbi School of Engineering sgutta@usc.edu.
  • J Acharya
    Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California.
  • M S Shiroishi
    Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California.
  • D Hwang
    Department of Radiology (J.A., M.S.S., D.H., K.S.N.), Keck School of Medicine, University of Southern California, Los Angeles, California.
  • K S Nayak
    From the Ming Hsieh Department of Electrical and Computer Engineering (S.G., K.S.N.), Viterbi School of Engineering.

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