Evaluation of 18 F-FET-PET and perfusion MRI texture features in brain tumor grades

The aim of this study is evaluating PET using the $^{18}$ Flabeled amino acid O-($2-^{18} {F} -$fluoroethyl)-L-tyrosine (FET-PET) and perfusion-weighted MRI (PWI) texture features for the differentiation between high- and low-grade gliomas. Twenty-seven patients with gliomas underwent $^{18} {F} -$FET-ET and perfusion MR Imaging. FETPET images and the perfusion maps were co-registered with perfusion images. The tumor’s volume of interest (VOI) was delineated by a threshold-based method. In the next step, texture feature analysis was done on the created VOIs based on the gray levels. For the evaluation of the extracted texture parameters, principal component analysis was applied to identify the best features for the classification between low- and high-grade gliomas by calculating the score and coefficient of each feature for further use. With PCA we precisely calculated the score and weight of each feature and showed how these top score features are correlated and enough to making decision on machine learning feature extraction/selection. Based on a previous identification based on database approach [1] we prepared a MLDB (machine learning database) feature extraction based on these top scored feature to train and test further data from this field.