## 2 Materials and Methods
### Feature Extraction  
This is an important step to develop a conventional machine learning model is to extract features and to select the most relevant features based on the type of the problem at hand. Features play an important role in image processing. Extracted features provide a convenient way in converting an input image to a set of quantitative values [12]. Different features have special supremacy over diagnosis criterion [13]. In this study, we employed different texture feature extraction methods, such as neighbourhood gray-tone difference matrix features, gray length size zone matrix, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRM), in order to extract appropriate and relevant information from an input set of images. These features are shown to be very effective in the past in differentiating between the cancerous and healthy cases [16] and predicting survival outcome not only in the pathology domain, but also in MRI images [4; 17; 18]. Local binary pattern (LBP) features: The original LBP [19] was proposed by the University of Oulu's computer vision group in Finland. It was initially used to classify texture images and since gained popularity in the domains of computer vision and pattern recognition. The original LBP was defined in a 3 \(\times\) 3 rectangular region, which significantly constrained texture information extraction and made it impossible to produce rotation invariant features. As a result, Ref. [20] transformed LBP's rectangular neighbourhood structure into a circular neighbourhood structure and used uniform sampling of \(p\) neighbourhood points in a circular local structure with radius R. However, the size of LBP  
Figure 1: Schematic diagram of proposed survival prediction method, employing state-of-the-art feature extraction methods and an ensemble of machine learning regression methods. characteristics grows exponentially with the number of neighbourhoods, severely limiting computation performance and storage space. Neighbourhood grey-tone difference matrix (NGTDM) features: texture features obtained from an NGTDM neighbourhood gray-tone difference matrix to classify cancerous nodules in a patient could be used as predictors; the value of those texture features expressed the intensity variations between a voxel and its neighbouring voxels, according to the definition of those NGTDM textural features [21]. Grey level size-zone matrix (GLSZM) features: A standardized texture is made up of vast regions with the same intensity, rather than smaller groupings of pixels or segments in any given direction. To account for this feature, the size of each region containing pixels of the same intensity level has to be taken into account in a matrix. This matrix was created using run length matrix principle: value of the matrix's \((s,n)\) square equals the number of regions of size s and grey level \(n\)[22]. Grey level run length matrix (GLRLM) features: The grey level run length matrix is a statistical texture characterisation technique [23]. This approach involves to count pixel segments number with same strength in a particular direction and then displaying the results in a matrix [22]. The grey level run length matrix (GLRLM) is a matrix that may be used to extract texture characteristics for texture analysis [24]. The GLRLM approach is a technique applied to obtained the higher order statistical texture characteristics. A grey level run is defined as a line of pixels with the same intensity value that runs in a certain direction [24]. The grey level run length is defined by number of such pixels, and number of occurrences is referred to as run length value. In this context, a run length is defined as number of neighbouring pixels with same grey intensity in a certain direction. Grey level co-occurrence matrix (GLCM) features: GLCMs are often referred to as spatial grey level dependence matrices. It is a popular second-order statistical method for extracting texture information from images.