Tumor recurrence in Glioblastoma Multiforme patients is nearly inevitable. This study analyzes the texture-based radiomic features of the peritumoral tumor habitat to identify the distinguishing features of tissue conducive to tumor growth. Peritumoral region and FLAIR edema are segmented and divided into subregions, which are labeled positive if it contains many voxels that appear normal, but subsequently becomes tumor. Texture features are extracted from each subregion and selected for use in an SVM. The classifier achieved 74.81% accuracy and .81 AUC. Texture analysis of peritumoral region can predict cancer growth and enables better treatment and surgical planning.
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