Texture Descriptors to distinguish Radiation Necrosis from Recurrent Brain Tumors on multi-parametric MRI

Differentiating radiation necrosis (a radiation induced treatment effect) from recurrent brain tumors (rBT) is currently one of the most clinically challenging problems in care and management of brain tumor (BT) patients. Both radiation necrosis (RN), and rBT exhibit similar morphological appearance on standard MRI making non-invasive diagnosis extremely challenging for clinicians, with surgical intervention being the only course for obtaining definitive "ground truth". Recent studies have reported that the underlying biological pathways defining RN and rBT are fundamentally different. This strongly suggests that there might be phenotypic differences and hence cues on multi-parametric MRI, that can distinguish between the two pathologies. One challenge is that these differences, if they exist, might be too subtle to distinguish by the human observer. In this work, we explore the utility of computer extracted texture descriptors on multi-parametric MRI (MP-MRI) to provide alternate representations of MRI that may be capable of accentuating subtle micro-architectural differences between RN and rBT for primary and metastatic (MET) BT patients. We further explore the utility of texture descriptors in identifying the MRI protocol (from amongst T1-w, T2-w and FLAIR) that best distinguishes RN and rBT across two independent cohorts of primary and MET patients. A set of 119 texture descriptors (co-occurrence matrix homogeneity, neighboring gray-level dependence matrix, multi-scale Gaussian derivatives, Law features, and histogram of gradient orientations (HoG)) for modeling different macro and micro-scale morphologic changes within the treated lesion area for each MRI protocol were extracted. Principal component analysis based variable importance projection (PCA-VIP), a feature selection method previously developed in our group, was employed to identify the importance of every texture descriptor in distinguishing RN and rBT on MP-MRI. PCA-VIP employs regression analysis to provide an importance score to each feature based on their ability to distinguish the two classes (RN/rBT). The top performing features identified via PCA-VIP were employed within a random-forest classifier to differentiate RN from rBT across two cohorts of 20 primary and 22 MET patients. Our results revealed that, (a) HoG features at different orientations were the most important image features for both cohorts, suggesting inherent orientation differences between RN, and rBT, (b) inverse difference moment (capturing local intensity homogeneity), and Laws features (capturing local edges and gradients) were identified as important for both cohorts, and (c) Gd-C T1-w MRI was identified, across the two cohorts, as the best MRI protocol in distinguishing RN/rBT.