A radiomics approach to distinguish non-contrast enhancing tumor from vasogenic edema on multi-parametric pre-treatment MRI scans for glioblastoma tumors

Abstract

Glioblastoma (GBM) is a highly aggressive tumor with a heterogeneous tumor micro-environment that extends beyond the visible tumor margin and is known to play a substantial role in GBM recurrence. For instance, it is often difficult to distinguish infiltrating non-contrast enhancing tumor (nCET) from peritumoral edema due to their confounding appearances on T2w/FLAIR MRI scans. Thus, nCET is often left unresected and contributes to over 90% of recurrences in GBM tumors that occur within 2-cm of the resected tumor margin. Histopathologically, the infiltrative nCET has high cellularity compared to vasogenic edema. This work explores the hypothesis that these histopathological changes may be reflected on routine imaging as subtle micro-architectural texture differences, that can be captured via radiomic features, allowing for differentiating nCET from vasogenic edema on routine pre-treatment MRI scans (Gd-T1w, T2w, FLAIR). Our radiomic analysis involved registering the preoperative MRI sequences of GBM patients from two institutions to a healthy MNI atlas. In the absence of histopathological confirmation,‘ground truth’ for nCET region of interest (ROI) on pre-treatment scans was defined as the site of future recurrence (as established on post-treatment scan with histopathologically-confirmed recurrence). Similarly, the ROI for vasogenic edema was defined as a region far from the site of recurrence, within the FLAIR/T2w hyperintensity edema on pre-treatment MRI. For every nCET and vasogenic edema ROI, a total of 316 3D radiomic features (e.g., Haralick, Laws, Gabor) were extracted from every MRI sequence. Feature pruning was conducted on the features’ statistics (median, variance, skewness, kurtosis) and a sequential feed forward classification scheme that employed a support vector machine classifier was applied. The FLAIR sequence yielded the highest accuracy in distinguishing nCET from vasogenic edema ROIs with accuracy of 91.3% and 78.5% on training (n = 25) and test (n = 30) sets respectively. Additionally, combining radiomic features from all three MRI sequences yielded an accuracy of 92.3% and 89.3% on the training and test sets, respectively. These results show that our radiomic approach may allow for reliable distinction of nCET from vasogenic edema on routine MRI scans, and thus may aid in improving treatment management of GBM tumors.