## 3 Experimental Results
In the classification analysis, five metrics are considered: FP Rate, Precision, Recall, F-Measure and ROC Area. \begin{table}
\begin{tabular}{c c c c} \hline \# **Radiomic Feature** & **Wald–Wolfowitz** & **Mann–Whitney** & **Test Result** \\ \hline \([F1-F14]\) & \(p<0.001\) & \(p<0.001\) & passed \\ \([F15-F16]\) & \(p>0.10\) & \(p<0.001\) & not passed \\ \(F17\) & \(p<0.001\) & \(p<0.001\) & passed \\ \(F18\) & \(p>0.10\) & \(p<0.001\) & not passed \\ \hline \end{tabular}
\end{table} 
Table 2: Wald–Wolfowitz and Mann–Whitney null hypothesis \(H_{0}\) test results. The FP Rate (i.e., False Positive Rate) is computed as the ratio between the number of examples wrongly classified as belonging to class X and the total number of samples not belonging to class X:  
\[\mathit{FP\ Rate}=\frac{\mathit{fp}}{\mathit{fp}+\mathit{tn}}\]  
where _fp_ indicates the number of false positives (for instance whether we are evaluating the \(4+4\) [PERSON] score model, a \(3+4\) MRI labelled by the proposed method as a \(4+4\) [PERSON] score prostate cancer) and \(\mathit{tn}\) the number of false negatives (for instance whether we are evaluating the \(4+4\) [PERSON] score model, a \(3+4\) MRI labelled by the proposed method as not related to the \(4+4\) [PERSON] score prostate cancer). The precision has been computed as the proportion of the examples that truly belong to class X among all those which were assigned to the class. It is the ratio of the number of relevant records retrieved to the total number of irrelevant and relevant records retrieved:  
\[\mathit{Precision}=\frac{\mathit{tp}}{\mathit{tp}+\mathit{fp}}\]  
where _tp_ indicates the number of true positives, and _fp_ indicates the number of false positives. The recall has been computed as the proportion of examples that were assigned to class X, among all the examples that truly belong to the class, i.e., how much of the class was captured. It is the ratio of the number of relevant records retrieved to the total number of relevant records:  
\[\mathit{Recall}=\frac{\mathit{tp}}{\mathit{tp}+\mathit{fn}}\]  
where _tp_ indicates the number of true positives, and _fn_ indicates the number of false negatives (for instance whether we are evaluating the \(4+4\) Gleason score model, a \(4+4\) MRI labelled by the proposed method as not related to the \(4+4\) Gleason score for prostate cancer). The F-Measure is a measure of a test's accuracy. This score can be interpreted as a weighted average of the precision and recall:  
\[\mathit{F-Measure}=2*\frac{\mathit{Precision}*\mathit{Recall}}{\mathit{ Precision}+\mathit{Recall}}\]  
The Roc Area is computed as the probability that a positive patient chosen in a random way is classified above a negative patient chosen in a random way. Table 3 shows the classification results. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Algorithm** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **Roc Area** & **Gleason** \\ \hline \multirow{4}{*}{_C 4.5_} & 0.035 & 0.848 & 0.740 & 0.790 & 0.908 & 3 + 4 \\  & 0.103 & 0.852 & 0.852 & 0.852 & 0.912 & 4 + 3 \\  & 0.027 & 0.885 & 0.874 & 0.880 & 0.932 & 4 + 4 \\ \hline \multirow{4}{*}{_SVM_} & 0.018 & 0.727 & 0.185 & 0.295 & 0.583 & 3 + 4 \\  & 0.835 & 0.448 & 0.976 & 0.615 & 0.570 & 4 + 3 \\  & 0.008 & 0.844 & 0.170 & 0.283 & 0.581 & 4 + 4 \\ \hline \multirow{4}{*}{_Gaussian_} & 0.728 & 0.460 & 0.893 & 0.608 & 0.673 & 3 + 4 \\  & 0.049 & 0.508 & 0.191 & 0.277 & 0.702 & 4 + 3 \\  & 0.006 & 0.692 & 0.057 & 0.105 & 0.739 & 4 + 4 \\ \hline \multirow{4}{*}{_RandomForest_} & 0.080 & 0.886 & 0.899 & 0.893 & 0.918 & 3 + 4 \\  & 0.057 & 0.803 & 0.873 & 0.837 & 0.924 & 4 + 3 \\ \cline{1-1}  & 0.021 & 0.888 & 0.879 & 0.942 & 0.944 & 4 + 4 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance results.