# An Investigation on Radiomics Feature Handling for HNSCC Staging Classification
## 2 Materials and Methods
### Radiomics Feature Extraction  
The open-source python package Pyradiomics [37] has been used for feature extraction from the ROIs of both CT and SUV maps. Image normalization and resampling have been performed to equally range the data in order to improve the analyses and make them uniform. Notably, both original and filtered images have been considered for the extraction process, thus collecting 1409 radiomics features. Here, we extracted: first order features (18), reporting image histogram statistics; 3D shape features (14), yielding morphological properties; and texture features (74), assessing surface textures. Belonging to the last features group, the Gray-Level Co-Occurrence Matrix (GLCM) shows how the discretized gray-level values of neighboring pixels are distributed along the image directions (24); the Gray-Level Size Zone Matrix (GLSZM) measures image gray-level zones, i.e., the number of connected voxels sharing the same gray-level intensity (16); the Neighboring Gray  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Variable**} & \multicolumn{2}{c}{\(DS_{1}\) **(n = 182)**} & \(DS_{2}\) **(n = 19)** & **Total (n = 201)** \\ \hline \multirow{2}{*}{pN-Stage} & Positive & 154 & 17 & 171 \\ \cline{2-5}  & Negative & 28 & 2 & 30 \\ \hline \multirow{3}{*}{pT-Stage} & T1/T2 & 106 & 9 & 115 \\ \cline{2-5}  & T3 & 50 & 1 & 51 \\ \cline{2-5}  & T4 & 26 & 9 & 35 \\ \hline \multirow{3}{*}{Overall Stage} & II & 14 & 1 & 15 \\ \cline{2-5}  & III & 26 & 1 & 27 \\ \cline{2-5}  & IV & 142 & 17 & 159 \\ \hline \multirow{3}{*}{Gender} & Male & 135 & 15 & 150 \\ \cline{2-5}  & Female & 47 & 4 & 51 \\ \hline \multirow{3}{*}{Age} & Average & 62.5 & 66.1 & 62.8 \\ \cline{2-5}  & St. Dev. & 9.3 & 9.6 & 9.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution for the chosen outcomes, gender and age.  
Tone Difference Matrix (NGTDM) quantifies the difference between a gray value and the average gray value of its distance (d) neighbors (5); the Gray-Level Dependence Matrix (GLDM) evaluates gray-level dependencies in an image, i.e., the number of connected voxels depending on the center voxel in a d distance (14) and, finally, the Gray-Level Run-Length Matrix (GLRLM) quantifies gray-level runs as the length in number of pixels of consecutive pixels with the same gray-level value (16). Feature definitions, formulas and meaning, to which we refer, are in accordance with features defined by the Image Biomarker Standardization Initiative (IBSI) [38].  
Furthermore, the original images have been filtered using Laplacian of Gaussian (LoG) filters and wavelet transform. Log-filtered images have been obtained by convolving the image with the second derivative (Laplacian) of a Gaussian kernel. Thus, the resulted images are derived with respect to a specified sigma value, here in a range from 0.5 to 3 with 0.5 pitch. The filter allowed us to analyze areas with rapidly changing intensities, enhancing edges, which can be full of information. Sigma value represents the LoG width: when it is low, more fine textures can be emphasized; otherwise, the coarse ones will be considered [39]. A wavelet transform has been used to decompose volumetric images into eight decomposed volumes of images, i.e., LLL, LLH, LHL, LHH, HLL, HLH, HHL and HHH, where L and H are low- and high-frequency signals, respectively. The transformation has been applied along the three axes XYZ, e.g., LLH means that the volume of the images is transformed by using the low-pass filters on the X and Y axis, and a high-pass filter on the Z one [40]. Per each decomposition, the previous listed features, except the shape 3D ones, have been extracted. Finally, we have totalized 1409 radiomics features.  
Figure 1 shows the flowchart of the proposed method.