title: Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features.
abstract: Purpose:The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients.Materials and Methods: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.Results: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 andpost-hocDunn-Šidák 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.Conclusions: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-90th percentile, and Cerebral Blood Volume (CBV) 90th 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.
authors.affiliation: Department of Electrical Engineering (ESAT), Signal Processing and Data Analytics, STADIUS Center for Dynamical Systems, KU LeuvenLeuven, Belgium; imecLeuven, Belgium.
authors.forename: Adrian
authors.initials: A
authors.lastname: Ion-Mărgineanu
authors.affiliation: Department of Radiology, University Hospitals of Leuven Leuven, Belgium.
authors.forename: Sofie
authors.initials: S
authors.lastname: Van Cauter
authors.affiliation: Department of Electrical Engineering (ESAT), Signal Processing and Data Analytics, STADIUS Center for Dynamical Systems, KU LeuvenLeuven, Belgium; imecLeuven, Belgium.
authors.forename: Diana M
authors.initials: DM
authors.lastname: Sima
authors.affiliation: Department of Electrical Engineering (ESAT), PSI Center for Processing Speech and Images, KU Leuven Leuven, Belgium.
authors.forename: Frederik
authors.initials: F
authors.lastname: Maes
authors.affiliation: Department of Radiology, University Hospitals of Leuven Leuven, Belgium.
authors.forename: Stefan
authors.initials: S
authors.lastname: Sunaert
authors.affiliation: Department of Imaging and Pathology, Biomedical MRI/MoSAIC, KU Leuven Leuven, Belgium.
authors.forename: Uwe
authors.initials: U
authors.lastname: Himmelreich
authors.affiliation: Department of Electrical Engineering (ESAT), Signal Processing and Data Analytics, STADIUS Center for Dynamical Systems, KU LeuvenLeuven, Belgium; imecLeuven, Belgium.
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
keywords: boosting classifiers; follow-up; glioblastoma multiforme; magnetic resonance imaging; multi-parametric