AUTHOR=Kim Donnie , Wang Nicholas , Ravikumar Viswesh , Raghuram D. R. , Li Jinju , Patel Ankit , Wendt Richard E. , Rao Ganesh , Rao Arvind 

TITLE=Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=13

YEAR=2019

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00052

DOI=10.3389/fncom.2019.00052

ISSN=1662-5188

ABSTRACT=<p>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.</p>