Machine-learning based classification of glioblastoma using dynamic susceptibility enhanced MR image

A classification method that integrates delta-radiomic features, DSC MRI, and random forest approach on the glioblastoma classification task is proposed. 25 patients, 13 high and 12 low-grade gliomas, who underwent the standard brain tumor MRI protocol, including DSC MRI, were included. Tumor regions on all DSC MRI images were registered to and contoured in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) images. These contours and its contralateral regions of the normal tissue were used to extract delta-radiomic features before applying feature selection. The most informative and non-redundant features were selected to train a random forest to differentiate high-grade (HG) and low-grade (LG) gliomas. These were then fed into a leave-one-out cross-validation random forest to classify these tumors for grading. Finally, a majority-voting method was applied to reduce binarization bias and to combine the results of various feature lists. Analysis of the predictions showed that the reported method consistently predicted the tumor grade of 24 out of 25 patients correctly (0.96). Finally, the mean prediction accuracy was 0.95 +/- 0.10 for HG and 0.85 +/- 0.25 for LG. The area under the receiver operating characteristic curve (AUC) was 0.94. This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades. The radiomic features from DSC MRI may be used to elucidate the underlying tumor biology and response to therapy.