## 2 Related Work  
Radiomics, the extraction of quantitative features from medical images using data characterization algorithms, has the potential to provide more relevant information, improve decision outcomes and avoid overdiagnosis and overtreatment. A full radiomic study follows a pipeline initially proposed by [PERSON] et al. [13] involving several steps, as exemplified in Figure 1.  
Many imaging modalities are of great value in screening PCa and improving diagnosis and prognosis outcomes [14]. MRI provides superior soft-tissue contrast resolution when compared to other imaging modalities. It is the selected imaging modality by Prostate Imaging Reporting and Data System (PIRADS). Most radiomic studies are focused on MRI with the PCa clinical significance as a model endpoint [15; 16; 17; 18; 19; 20; 21]. The combination of T1 and T2 weighted sequences (multi-parametric Magnetic Resonance Imaging (mpMRI)) allows us to overcome the poor correlation between MRI signal intensities and tissue properties. With this in mind, several authors evaluated the use of MRI for PCa patient stratification with promising results [16; 17; 18; 19], although the introduction of clinical outcomes such as PSA or GS introduced some issues [20]. [PERSON] and [PERSON] [22] topped the PROSTATEx-2 2017 challenge with a quadratic-weighted kappa score of 0.2772, developing a new feature selection method for PCa aggressiveness assessment. [PERSON] et al. [23] sought a model to evaluate Intensity Modulated Radiation Therapy (IMRT) PCa treatment responses using T2w and Apparent Diffusion Coefficient (ADC) maps in an attempt to personalize the PCa treatment evaluation framework.  
PET provides insights into the pathological responses to some types of cancers with the addition of a radio-tracer and a viable tool for diagnosing, staging and grading. For radiomic PCa studies, researchers focused on evaluating lymph node involvement, metastasis, GS and extra-capsular extension [24]. [PERSON] et al. [25] evaluated tumour heterogeneity with 18F-Cho-PET/CT radiomics and introduced a novel feature selection method (a mixed descriptive-inferential sequential approach).  
CT seems to be a poor candidate for radiomic studies since it lacks metabolic manifestation and soft-tissue contrast. However, the spatial distribution provided by the CT could be used as a virtual biopsy for patient risk stratification [26]. In a recent work, [PERSON] et al. [11] evaluated the use of CT based radiomics for PCa aggressiveness assessment. With a dataset of 44 PCa patients, [PERSON] et al. [11] extracted features using pyradiomics [10] and Local Image Features Extraction (LIFEx) [27]. Unable to find a radiomic signature for RG stratification, they used Principal Component Analysis (PCA) and evaluated several kernels to build a model with a Support Vector Machine (SVM). The best results were obtained with pyradiomics with a maximum Area Under the Receiver Operating Characteristic (AUROC) value of 0.88 for both low/very low and high/very high RG.  
CBCT is used for patient positioning verification procedures before EBRT treatment and therefore is freely available. [PERSON] et al. [12] were the first to study the use of CBCT radiomics to build models predicting tumour staging, GS, PSA levels, risk category and biochemical recurrence with promising results. In this work, we intend to evaluate the use  
Figure 1: Radiomic studies pipeline.  
of CBCT radiomics to distinguish between favourable and unfavourable PCa cases. An unevaluated scenario that may provide an EBRT treatment effectiveness monitoring tool is used.