Figure 2 Axial T1-weighted fat saturation image slice of the abdomen of a typical subject (left), and ROI drawn on lymphoma mass (right). Fisher coefficient (Fisher) and classification error probability (POE) combined with average correlation coefficients (ACC) provided Selleck SBE-��-CD by MaZda were used to identify the most significant texture features to discriminate and classify the three evaluation stages of lymphoma tissue. Ten texture features were chosen by both methods (Fisher, POE+ACC). This feature selection was performed separately for the T1- and T2-weighted image sets. In these subgroups feature selection was run for the following imaging stages:
combination of all imaging timepoints (E1, E2, and E3), and all combinations of the two aforementioned. Slice thickness was not taken into account. Volumetric analysis The volumetry of the solid lymphoma masses was evaluated between diagnostic stage (E1) and after the first treatment (E2). The masses were selected for evaluation before chemotherapy. The same masses were followed after the first treatment. Volumetric analysis based on MRI images was performed with semiautomatic segmentation LY411575 cell line software Anatomatic™  with region growing method. . Clinical parameters analyses The patients’ subjective views on their clinical symptoms was observed between two
stages: at the diagnosis and after the first treatment. The subjective views were set in two groups: symptoms unchanged Epacadostat or relieved. Grade of malignity was classed into two groups: 1) low; 2) high/intermediate. Tissue classification B11 application (version 3.4) of MaZda software package was used for texture data analysis and classification. Analyses were run between all combinations of imaging stages separately for T1- and T2-weighted images. Analyses were performed for combination of parameters selected automatically with Fisher and POE+ACC methods for 1) the specific imaging timepoint pair in question and 2) for all imaging stages in particular image type (T1-, T2-weighted). Feature standardization was used in B11, the mean value being subtracted from each feature and the
result divided by Dipeptidyl peptidase the standard deviation. Raw data analysis (RDA), principal component analysis (PCA), and linear (LDA) and nonlinear discriminant analysis (NDA) were run for each subset of images and chosen texture feature groups. B11 default neural network parameters were used. Nearest-neighbor (1-NN) classification was performed for the raw data, the most expressive features resulting from PCA and the most discriminating features resulting from LDA. Nonlinear discriminant analysis carried out the classification of the features by artificial neural network (ANN). These classification procedures were run by B11 automatically. Statistical analyses Statistical analyses were run for the texture features MaZda’s automatic methods (Fisher and POE+ACC) had shown to give best discrimination between imaging timepoints.