## 5 Experimental Results  
The main experiment described in Section 4 was applied to all possible combinations of classifiers and VOIs where the features are computed, summarised in Table 2. Furthermore, the experiments are run also using both the single ranking and the total ranking methods for feature selection. The use of four classifiers, three VOIs, and two feature selection ranking approaches results in twenty-four experiments, whose results are presented in Table 3. We measured the following performance metrics: \(accuracy=\frac{TP+TN}{P+N}\), the _area under the ROC curve_ (\(AUC\)), the \(precision=\frac{TP}{TP+FP}\) and the \(recall=\frac{TP}{TP+FN}\). Straightforwardly, \(TP\), \(TN\), \(P\), and \(N\) stands for the true positive, true negative, total number of positive samples, and total number of negative samples, respectively. Hereinafter, we also use \(FP\) and \(FN\) to denote the false positive and false negative, respectively. Let us recall that the positive and negative classes correspond dead and alive patients, respectively, as described in Section 3. In Table 3, the upper panel shows the performance of the twelve combinations of the classifiers and the VOIs when the single ranking method is used, whereas the lower panel reports the performance for the total ranking case. For the single ranking, the accuracy and AUC values range from 60.82% to 83.51% and from 61.06% to 82.78%, respectively. For the total ranking, the accuracy ranges from 44.33% to 77.32%, and the AUC ranges from 44.43% to 75.96%. The subsets of features selected in each combination range from two to nine descriptors for the single ranking and from two to 20 descriptors for the total ranking. Note that, in the total ranking case, we obtain a worse range of accuracy, AUC values and larger subsets of selected feature in comparison to the single ranking. This could be expected, since the single ranking approach provides a best feature subset optimised for both the classification algorithm and the considered VOI. A more detailed analysis of Table 3 suggests us other three observations. The first is that the performance on GTV in the single ranking is stable across the four classification algorithms. The same does not happen for the total ranking case. We deem this indicates that a system built on this VOI is particularly stable when a volume-specific optimization occurs for the best feature set selection. Secondly, the overall largest performance was obtained combining the classifier Adaboost with the CTV in the single ranking procedure. Compared to this result, the best result for the total ranking procedure was obtained combining the Random Forest Classifier with, again, the CTV. Such best results for single and total rankings are highlighted in bold in Table 3. As a third observation, we notice that, although both the best experiments exploit the CTV, the largest performance are given by the single ranking approach, where the best feature set was customized on the specific VOI analysed. We also run the ANOVA test among the VOIs both for the results attained by single and total ranking, whatever the classification paradigm used. When we get a significant result, i.e., at least one of the groups tested differs from the other groups, we proceed with Least Significant Difference (LSD) that detects the statistical differences among a group of results. In the case of results provided by the single ranking, we obtain a p-value equal to 0.0334, suggesting that the performance achieved using the different VOIs are not the same. The LSD tests show that the results of CTV+Adaboost are significant different (\(p<0.1\)) from the others in all cases, except for CTV+Random Forest, PTV+Random Forest, and PTV+XGBoost. In the case of the results provided by the total ranking, the p-value is \(1.586\times 10^{-5}\), suggesting again that there are differences between the VOIs.