Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM

Glioblastoma (GBM) is a markedly heterogeneous brain tumor and is composed of three main volumetric phenotypes, namely, necrosis, active tumor and edema, identifiable on magnetic resonance imaging (MRI). This paper assesses the usefulness of the GBM features detection by using semi-automatic segmentation and texture feature extracted from gray level co-occurrence matrix (GLCM). Feature vectors are then used for predicting GBM phenotypes based on nearest neighbors (NN) classifier. Simulation results for 22 patients show an accuracy of 75.58% for distinguishing GBM phenotypes based on the texture feature selection using the decision trees model. Preliminary texture analysis demonstrated that the texture feature based on the GLCM is promising to distinguish GBM phenotypes.