## 2 Materials and Methods
### Feature Extraction  
After VOI identification, the extraction of radiomics features was performed for each patient in the data set. One of the main points in radiomics is to increase the reproducibility of the extracted features. For this, the image biomarker standardization initiative (IBSI) [30], which is an independent international collaborative study towards the standardization of radiomics features for the purpose of high-throughput quantitative image analyses, has been introduced. For this reason, we used a comprehensive open source IBSI-compliant platform called PyRadiomics (Harvard Medical School, Boston, MA, USA) [31], which enables processing and extraction of radiomics features from medical image data using a large panel of engineered hard-coded feature algorithms, and currently is one of the most commonly used software for radiomics studies. PyRadiomics is implemented in Python, a language that has established itself as a popular open-source language for scientific computing, and which can be installed on any system. PyRadiomics provides a flexible analysis platform with a back-end interface allowing automation in data processing,feature definition, and batch handling. The Pyradiomics platform calculates different feature classes, namely first-order statistics, shape descriptors, and five texture classes: gray level cooccurrence matrix (GLCM), gray level run-length matrix (GLRLM), gray level dependence matrix (GLDM), gray level size-zone matrix (GLSZM) and neighboring gray level dependence matrix (NGLDM). Shape-based features are based on the VOI voxel representation and are independent of the distribution of gray level intensity in the image. They are used to describe the three-dimensional shape, size of the lesion and other geometric aspects such as volume, maximum diameter along different orthogonal directions, surface, tumor compactness, and sphericity (a measure of roundness). Specifically, compactness and sphericity describe how the VOI shape differs from that of a circle (for 2D analyses) or a sphere (for 3D analyses). Additionally, the surface area is calculated by triangulation (a process that produces a net of triangles that completely cover the tumor surface) and serves as a base for calculation of the surface-volume ratio: spiculated tumors show higher values than those of a round tumor of similar volume. Furthermore, flatness describes whether the surface of the object is flat or has raised areas or indentations. In short, these radiomics features provide physical measurements and significantly contribute to clinical outcome. First-order statistical features describe the frequency distribution of voxels within a VOI. This information can be obtained from the histogram of gray-level intensity values; for this, they are referred as \"histogram-based\" features. Sophisticated features include skewness and kurtosis, which describe the shape of the intensity distribution of data: skewness reflects the asymmetry of the data distribution curve to lower or higher values than the mean one (negative skew and positive skew, respectively), whereas kurtosis reflects the tail of a data distribution with respect to a gaussian distribution due to outliers. Other features include histogram entropy and uniformity (also called energy). Texture analysis is a key concept in radiomics. It refers to wide variety of quantitative methods that are used to assess the relative voxel positions within the image to derive texture features. As a result, the texture features provide information on the spatial organization of color or intensities in an image or a selected region of an image. The texture is a linked set of voxels fulfilling a given gray level property that occurs repeatedly in an image region thus creating a textured region. Due to the fact that the texture is characterized by the spatial distribution of gray levels in a neighborhood, it cannot be defined for a point. Texture features are sub-categorized according to particular matrices from which they are obtained.