## 5 Experimental Results
The method with the best AUC, i.e., CTV+Random Forest, shows performance statistically different from all the others except for CTV+AdaBoost, PTV+AdaBoost, GTV+Random Forest, PTV+Random Forest, CTV+XGBoost, and PTV+XGBoost. Let us now turn the attention to the radiomics signature underlying the best case, i.e., CTV+Adaboost in single ranking: as shown in the first column of Table 4 it is composed of eight features belonging to the LBP set, and one belonging to the GLCM set. In detail, such features are: \"uniform 3D LBP kurtosis\", which estimates the shape of the distribution of all patterns discharging the noisy patterns, \"3D LBP energy\", which represents the homogeneity of the patterns, \"rotation invariant 3D LBP absolute maximum\", which is the absolute maximum value among the patterns, regardless of their rotation, \"3D LBP energy around the absolute maximum\", which estimates the homogeneity of patterns around the absolute maximum value, \"rotation invariant 3D LBP energy\", which is the homogeneity of the patterns without considering their rotation, \"uniform 3D LBP energy around the relative maximum\", which estimates the homogeneity of pattern around a relative maximum value discharging the noisy patterns, \"uniform 3D LBP entropy\", which is a measure of the variety of patterns discharging the noisy ones, and \"uniform 3D LBP skewness\", which measures the asymmetry of distribution of all patterns discharging noisy patterns, and \"inverse GLCM in direction \((-1,-1,0)\)\", which measures the inverse of grey level attenuation in the ROI over the direction \((-1,-1,0)\). Turning our attention to the newly introduced set of features, i.e., 3D LBPs, and still focusing on the single ranking approach, these descriptors were always chosen in the best feature subsets, regardless of the classifier and the VOIs under consideration. For the sake of brevity, we hereby do not report the full list of features chosen in each of the 24 experiments. However, to provide a synthetic analysis of the best feature sets, the second and the third columns of Table 4 show how many times each feature in the first column (i.e., the radiomic signature of the best case) was included in the best feature set using the \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline _Single_ & \multicolumn{3}{c}{**AdaBoost**} & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**XGBoost**} \\ \cline{2-11} _Ranking_ & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** \\ \hline Accuracy & 70.10 & **83.51** & 71.13 & 70.10 & 60.82 & 63.92 & 73.20 & 78.35 & 77.32 & 68.04 & 71.13 & 76.29 \\ \hline AUC & 70.13 & **82.78** & 70.11 & 70.13 & 61.06 & 64.09 & 71.81 & 77.29 & 76.54 & 66.70 & 70.11 & 75.02 \\ \hline Precision & 69.81 & **90.57** & 81.13 & 69.81 & 58.49 & 62.26 & 86.79 & 88.68 & 84.91 & 67.19 & 70.49 & 73.44 \\ \hline Recall & 74.00 & **81.36** & 70.49 & 74.00 & 65.96 & 68.75 & 70.77 & 75.81 & 76.27 & 81.13 & 81.13 & 88.68 \\ \hline \multicolumn{11}{c}{_Total_} & \multicolumn{3}{c}{**AdaBoost**} & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**XGBoost**} \\ \cline{2-11} _Ranking_ & **GTV** & **CTV** & **PTV** & **GTV** & **GTV** & **GTV** & **GTV** & **GTV** & **GTV** & **GTV** & **PTV** \\ \hline Accuracy & 64.95 & 72.16 & 68.04 & 44.33 & 58.76 & 65.98 & 75.26 & **77.32** & 74.23 & 64.95 & 72.16 & 74.23 \\ \hline AUC & 64.84 & 71.83 & 67.47 & 44.43 & 59.18 & 65.59 & 73.89 & **75.96** & 73.33 & 62.52 & 70.09 & 72.94 \\ \hline Precision & 66.04 & 75.47 & 73.58 & 43.40 & 54.72 & 69.81 & 88.68 & **90.57** & 83.02 & 62.67 & 68.06 & 71.88 \\ \hline Recall & 68.63 & 74.07 & 69.64 & 48.94 & 64.44 & 68.52 & 72.31 & **73.85** & 73.33 & 88.68 & 92.45 & 86.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System performance in all possible combinations.