Other studies reported a substantial difference in intra-tumoral heterogeneity between HPV-positive and HPV-negative OPSCC primary sites in radiological imaging [13; 14; 15]. Unfortunately, overlapping radiological characteristics do not allow one to precisely predict HPV status [16]. Radiomics is a non-invasive technique which implies the extraction of different quantitative features from routine medical images (e.g., Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans), their selection and subsequent analysis for correlation with clinical data [17; 18]. The extracted features describe characteristics such as tumor signal intensity, shape and texture patterns and provide reader-independent data for predictive modeling [19]. The feature-based radiomic workflow firstly relies on the _determination_ of imaging modality, followed by image processing, which represents the attempt at homogenizing imaging for feature extraction according to pixel spacing or gray-level intensities, and then by the Region of Interest (ROI) or Volume of Interest (VOI) segmentation, for the definition of those regions where features are calculated. Then, quantitative features can be extracted with a possible large number (even more than 1000) from a single ROI, since the process is based on a huge number of mathematical operations. Features can be divided into many subgroups [20]:  
* _Shape features_: these are related to the geometric properties of the ROI/VOI (volume, diameter, sphericity and compacity);
* _First order (histogram) features_: these describe the distribution of voxel intensities within the image region (mean, median, skewness, kurtosis);
* _Second order texture features_: these consider the statistical relationship between neighboring voxels or groups of voxels within the segmented lesion. Many matrices, such as the Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Neighborhood Gray-Level Different Matrix (NGLDM), can be used for feature extraction [21]. GLCM provides information about pixel pair distribution within the image; GLRLM identifies the length of consecutive voxels with the same intensity in a pre-set direction in the image; NGLDM analyzes the difference between a gray value and the average gray value of its neighbors within a certain distance [22]. * _Higher-order texture features_: additional mathematical transformations are used to highlight specific aspects of the ROI. This can lead to a virtually endless number of features among which fractals, SUV metric for PET specific applications [23; 24], Minkowski functionals, wavelet transform and Laplacian transforms of Gaussian-filtered images are included [25; 26]. After feature extraction, the most relevant features must be selected among thousands available; redundant or non-reproducible features are generally excluded and feature selection or dimensionality reduction algorithms are usually adopted. Common examples involve hierarchical clustering, principal component analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regularized logistic regression and maximum relevance minimum redundancy filtering [27]. The modeling process then follows in the radiomic workflow. In this phase, reproducible radiomic features can be used to build radiomic models for clinical selected endpoints. In this context, machine learning is the most common approach to radiomic-based prediction; it is based on training a model by showing it examples of input-output behavior so that the system learns the desired relationship without explicit manual programming. Numerous machine learning classifiers have therefore been developed, all utilizing many algorithms to find the optimal performance across repeated iterations of training and testing [27]. After model building, model independent validation is usually performed. In this step, the radiomic-based model is applied to datasets on which it was not trained. Internal validation refers to the performance in patients from a similar population to where the sample originated from.