## 5 Conclusions  
For PCa, EBRT is a curative option for localized and locally advanced disease and a palliative option for metastatic low-volume disease [6]. Currently, the only triggers for recurrence or treatment effectiveness monitoring are the PSA blood test or redoing a Transrectal Ultrasound Guided Biopsy (TRUS). With radiomics, a quantitative analysis of medical images may have similar prognostic power to phenotypes and gene protein signatures [9]. Interest in radiomics has been increasing and for PCa, it has been mainly  
Figure 7: Selected features frequency.  
focused on MRI in the initial staging and grading. However, during the EBRT, CBCTs are freely available, as they are used for patient positioning and setup verifications.  
The value of CBCT-based radiomics was evaluated to distinguish between favourable and unfavourable prognosis for patients initially classified as intermediate risk. Such a tool may provide added value to monitor and trigger possible changes in EBRT outcomes. Following the current methods of feature selection and classification for PCa radiomics, 98 pipelines were evaluated. The results seem to suggest that selecting features from an LR model, combined with a bagging (SVC) classifier, provided good performance. Although using 43 features, it lacks the potential to offer explainability. In this sense, a better approach seems to be using a percentile feature selection method and an AdaBoost classifier. This pipeline presents an AUROC of \(0.79\pm 0.27\) and an accuracy of \(0.80\pm 0.19\) and high values of precision and recall, being the most balanced of the evaluated pipelines.  
The skewness seems to be the most frequently selected feature, considering each fold in the cross-validation scheme. Although its true importance is yet to be evaluated, the fact it was selected in every fold of every feature selection method suggests it may provide some insights. Furthermore, the fact that only one CBCT was considered for each patient did not allow the evaluation of features reproducibility.  
Although the obtained results are promising, some improvements need to be made for a deeper evaluation. A grid-search cross-validation may provide fine-tuning of parameters and improved results. Furthermore, the used subset is quite small, and may benefit in the future from the inclusion of high-risk patients and the assessment of other models.  
CBCT-based radiomics may provide a baseline for an EBRT effectiveness assessment framework on ongoing treatment, improving outcomes and lowering recurrence rates regardless of the several limitations.  
Conceptualization, B.M.; methodology, B.M. and I.D.; software, B.M.; validation, B.M., I.D. and J.S.; formal analysis, B.M.; investigation, B.M.; resources, B.M., I.D., F.D. and J.S.; data curation, B.M. and F.D.; writing--original draft preparation, B.M.; writing--review and editing, B.M., I.D., F.D. and J.S.; supervision, I.D. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki. The study was approved by IPO Porto Healthcare Ethics Committee (protocol code CES.274/020 and date of approval 1 October 2020.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Informed Consent Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADC & Apparent Diffusion Coefficient \\ ANOVA & Analysis Of Variance \\ AUROC & Area Under the Receiver Operating Characteristic \\ BCR & Biochemical Recurrence \\ BN & Bayesian Network \\ BPH & Benign Prostic Hyperplasia \\ CBCT & Cone Beam Computed Tomography \\ CT & Computed Tomography \\ CTV & Clinical Target Volume \\ DRE & Digital Rectal Examination \\ EBRT & External Beam Radiotherapy \\ \end{tabular}