## 4 Results and Discussion  
The evaluation of the 98 pipelines was performed in a python environment, computing the AUROC and accuracy scores. Figure 4 shows the obtained AUROC. Figure 5 shows the obtained accuracy scores, and both present the corresponding mean standard deviations for the five fold cross-validation scheme (lighter colors mean a higher AUROC value as indicated in the color bar scale). The pipelines were built with a very specific subset and goal - to distinguish favourable and unfavourable cases from PCa patients classified as intermediate risk. The specificity of this approach may provide EBRT with a tool capable of monitoring the true effectiveness of the treatment. An initial unfavourable outcome may become, during treatment, a favourable case, possibly leading to adjustments in the treatment workflow. From the obtained results, a few pipelines do present good performance. In radiomic studies, there is always an issue with the reproducibility of the features. In the future, other CBCTs of each patient will be included in order to overcome this issue, as already performed by [PERSON] et al. (2012). Figure 3: Pipeline evaluation flowchart. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Parameters** \\ \hline AdaBoost & n\_estimators = 100 \\ Bagging (SVC) & base\_estimator = SVC(), n\_estimators = 10 \\ ExtraTrees & n\_estimators = 100 \\ GradientBoosting & n\_estimators = 100; learning\_rate = 1.0; max\_depth = 1 \\ HistGradientBoosting & loss = “log\_loss” \\ RandomForest & max\_depth = 2 \\ GaussianProcess & kernel = 1.0 * Radial-basis function(1.0) \\ KNeighbors & n\_neighbors = 3 \\ MLP & hidden\_layer\_sizes = (100, ); activation = “relu”; solver = “adam” \\ DecisionTree & criterion = “gini”; splitter = “best” \\ Bagging (ExtraTree) & base\_estimator = ExtraTreeClassifier() \\ SVC & kernel = “linear” \\ LogisticRegression & penalty = “12”; C = 1 \\ GaussianNB & var\_smoothing = \(1\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluated Classifiers. Still, results seem to suggest that some classifiers are not suited to this particular task. The HistGradientBoosting obtained an AUROC of 0.50 for every feature selection method. From the evaluated feature selection methods, the KBest (ANOVA) and RFECV (SVR) provided poor performances. On the other hand, the percentile feature selection method seems to have an overall decent performance, obtaining its best results when combined with an AdaBoost classifier with an AUROC of 0.79 \(\pm\) 0.27 and its worst when combined with an LR classifier with an AUROC of 0.72 \(\pm\) 0.25. Considering a threshold value of AUROC of 0.79, six pipelines were capable of obtaining good performance. Selecting features from an LR model and combining it with a Bagging (SVC) classifier or GaussianNB provided a pipeline with an AUROC value of \(0.82\pm 0.18\) and \(0.79\pm 0.17\), respectively. Furthermore, the univariate selection method combined with an SVC obtained an AUROC of \(0.80\pm 0.13\) and, with an ExtraTrees, of \(0.79\pm 0.21\). The Adaboost classifier combined with the baseline feature selection method, the variance threshold, provided an AUROC of \(0.80\pm 0.14\), and when combined with the percentile feature selection method, an AUROC of \(0.79\pm 0.27\). Table 6 highlights the selected pipelines obtained results. Figure 4: ROC values with a five fold-CV. Figure 5: Accuracy values with a 5-fold CV. The presented values are the mean values obtained for the five folds in the cross-validation. For these six pipelines, a deeper analysis was performed in order to evaluate the performance of each. Figure 6 presents the AUROC curves and corresponding values for each fold as well as the mean value. Furthermore, in grey, are the \(\pm 1\) standard deviation curves and, in dashed red, the 0.5 AUROC line.