Glioblastoma (GBM), the most commonly diagnosed malignant brain tumor in adults , has a poor prognosis, with a median survival of only 12-15 months and a high rate of recurrence . Poor prognosis and overall survival (OS) is attributable to the marked inter- and intra-tumoral genetic heterogeneity of GBM tumors [3–6]. Magnetic resonance imaging (MRI) holds great potential for characterizing the phenotypic heterogeneity of GBMs by inferring this from textural information and intensity variations in radiological images. Common techniques such as gadolinium contrast-enhanced T1-weighted imaging highlight perfusion variations in tumor images and advanced image-texture analysis may be able to characterize signal intensity variations within tumors. Texture analysis has many applications in medical image processing and provides one approach to quantify the distribution of gray-level patterns such as homogeneity, entropy, etc., within a set of imaging data . Multiple methods for assessing imaging features and characterizing pixel intensity distributions by quantifying gray levels have been described [8–11]. These methods allow for rigorous and reproducible derivation of detailed, pertinent information, and have been used to analyze MRI features, such as apparent diffusion coefficient, 2-dimensional (2D) spatial habitats , and texture features. These characteristics correlate with the grade of disease, patient survival, response to chemotherapy, and genetic and epigenetic status [12–18]. With recent advances in radiomics and radiogenomics (or imaging-genomcis), molecular and genetic heterogeneity can be inferred from MRI features by correlating imaging datasets with corresponding molecular and clinical information.