AUTHOR=Ion-Mărgineanu Adrian , Van Cauter Sofie , Sima Diana M. , Maes Frederik , Sunaert Stefan , Himmelreich Uwe , Van Huffel Sabine 

TITLE=Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 10 - 2016

YEAR=2017

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00615

DOI=10.3389/fnins.2016.00615

ISSN=1662-453X

ABSTRACT=The purpose of this paper is discriminating between tumour progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients.
Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI (i.e.\@ perfusion weighted MRI (PWI) and diffusion kurtosis MRI (DKI)) were acquired from 29 GBM patients treated with adjuvant therapy after surgery. We propose an automatic pipeline for processing advanced MRI data and extracting intensity-based histogram features and 3-D texture features using manually and semi-manually delineated regions of interest (ROIs). Classifiers are trained using a leave-one-patient-out cross validation scheme on complete MRI data. Balanced accuracy rate (BAR) values are computed and compared between different ROIs, MR modalities, and classifiers, using non-parametric multiple comparison tests.
Maximum BAR values using manual delineations are 0.956, 0.85, 0.879, and 0.932, for cMRI, PWI, DKI and all three MRI modalities combined, respectively. Maximum BAR values using semi-manual delineations are 0.932, 0.894, 0.885, and 0.947, for cMRI, PWI, DKI and all three MR modalities combined, respectively. After statistical testing using Kruskal-Wallis and post-hoc Dunn-{\v{S}}id{\'a}k analysis we conclude that training a RUSBoost classifier on features extracted using semi-manual delineations on cMRI or on all MRI modalities combined performs best. 
We present two main conclusions: (1) using T1 post-contrast (T1pc) features extracted from manual total delineations, AdaBoost achieves the highest BAR value, 0.956; (2) using T1pc-average, T1pc-90$^{th}$ percentile and Cerebral Blood Volume (CBV) 90$^{th}$ percentile extracted from semi-manually delineated contrast enhancing ROIs, SVM-rbf and RUSBoost achieve BAR values of 0.947 and 0.932, respectively. Our findings show that AdaBoost, SVM-rbf, and RUSBoost trained on T1pc and CBV features can differentiate progressive from responsive GBM patients with very high accuracy.