Radiomics features selection and modeling
All CT scans, derived from 78 patients, were considered technically
adequate for the purpose of the analysis and therefore included in the
study. In the figure 2 is shown the workflow of the study. Images were
acquired on two different scanners (SOMATOM Plus 4 before 2011, SOMATOM
Definition Flash after 2011, Siemens Healthineers, Erlangen, Germany).
Mean pixel spacing was 0.44 (0.42-0.45) while mean slice thickness 2.6
mm (1-5 mm). In total, 232 radiomics features have been firstly
extracted, belonging to the following feature classes: 20 statistical
features (grey-level histogram); 14 morphological features; 100 texture
features GLCM (grey level co-occurrence matrix); 66 texture features
GLRLM (grey level run length matrix); 32 texture features GLSZM (grey
level size zone matrix). After the Boruta selection procedure, 8
features were selected and addressed to the further step (Figure 3).
After Pearson correlation analysis, 2 features were lastly retained:
F_stat.mean (mean of voxel intensity histogram) and F_szm_2.5D.zsnu
(zone size non-uniformity, computed in the 2.5D version). According to
IBSI definition, F_stat.mean is a morphological intensity-based
statistical feature that describes the distribution of the grey levels
within the considered ROI.F_szm_2.5D.zsnu is a textural feature that
assesses the distribution of zone counts over the different zone sizes.
The uniformity of the zone sizes is low when the zone counts are
distributed equally along zone sizes [16].