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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Comput. Neurosci. | doi: 10.3389/fncom.2019.00052

Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Preoperative Multiparametric Magnetic Resonance Imaging

Donnie Kim1, Nicholas C. Wang2,  Visweswaran Ravikumar1,  D R. Raghuram1,  Jinju Li2,  Ankit Patel1, Richard E. Wendt1,  Ganesh Rao1 and  Arvind Rao2*
  • 1University of Texas MD Anderson Cancer Center, United States
  • 2University of Michigan, United States

This study compared the predictive power and robustness of texture, topological, and convolutional neural network (CNN) based image features for measuring tumors in MRI. These features were used to predict 1p/19q codeletion in the MICCAI BRATS 2017 challenge dataset. Topological data analysis (TDA) based on persistent homology had predictive performance as good as or better than texture-based features and was also less susceptible to image-based perturbations. Features from a pre-trained convolutional neural network had similar predictive performances and robustness as TDA, but also performed better using an alternative classification algorithm, k-top scoring pairs. Feature robustness can be used as a filtering technique without greatly impacting model performance and can also be used to evaluate model stability.

Keywords: Multiparametric MRI, image perturbation, radiomic features, Glioma, Persistent homology, 1p/19q codeletion

Received: 30 Apr 2019; Accepted: 11 Jul 2019.

Edited by:

Spyridon Bakas, University of Pennsylvania, United States

Reviewed by:

Carlos A. Silva, University of Minho, Portugal
Ken Chang, Massachusetts Institute of Technology, United States  

Copyright: © 2019 Kim, Wang, Ravikumar, Raghuram, Li, Patel, Wendt, Rao and Rao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Arvind Rao, University of Michigan, Ann Arbor, 48109, Michigan, United States, uk_arvind@ieee.org