## 3 Results  
The ICC for radiomic textural, morphological and dynamic features was excellent (median value 0.86, range 0.78-0.92), demonstrating the robustness of extracted features by DCE-MRI. Table 5 reports the diagnostic accuracy of textural, morphological and dynamic parameters in terms of AUC and \(p\)-value. Figure 2 shows ROC curve trends of significant features: mode, median, STD, range and GLN_GLRLM (Gray-Level Non-Uniformity, extracted by Gray-Level Run Length Matrix) among textural features, q2_MAD and q2_IQR (MAD and IQR calculated on q2), and WIN_MAD and WIN_IQR (MAD and IQR calculated on WIN) among dynamic ones. There are no significant morphological results (max AUC value of 0.61). Figure 3 shows the boxplots related to the above-mentioned parameters, to separate benign from malignant lesions. As regards multivariate analysis, only the most useful results for the purposes of this work will be reported. Table 6 reports the performance achieved by the best classifiers to discriminate benign from malignant lesions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **ACC** & **SENS** & **SPEC** & **PPV** & **NPV** & **AUC** \\ \hline \multicolumn{6}{c}{Performance for classifiers trained with balanced data (with ADASYN function) and all 48 textural features} \\ \hline LDA & 0.78 & 0.68 & 0.88 & 0.84 & 0.74 & 0.78 \\ \hline \multicolumn{6}{c}{Performance for classifiers trained with balanced data (with ADASYN function) and a subset of five robust morphological features} \\ \hline SVM & 0.75 & 0.80 & 0.72 & 0.74 & 0.79 & 0.80 \\ \hline \multicolumn{6}{c}{Performance for classifiers trained with balanced data (with ADASYN function) and a set of 21 robust dynamic features} \\ \hline SVM & 0.77 & 0.77 & 0.75 & 0.75 & 0.77 & 0.85 \\ \hline \multicolumn{6}{c}{Performance for classifiers trained with balanced data (with ADASYN function) and a set of 37 robust features} \\ \hline SVM & 0.88 & 0.86 & 0.89 & 0.89 & 0.86 & 0.93 \\ \hline \hline \end{tabular} Note. ACC = accuracy; SENS= sensitivity; SPEC= specificity; PPV= positive predictive value; NPV = negative predictive value.  
\end{table} 
Table 6: Performance of the best classifiers using textural, morphological and dynamic features alone and then together. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Textural Parameters** & **Symbol** & **AUC Values** & \(p\)**-Value** \\ \hline \multirow{3}{*}{First-order gray-level statistics} & MODE & - & 0.7 & 0.001 \\ \cline{2-5}  & STANDARD & & STD & 0.7 & 0.001 \\ \cline{2-5}  & RANGE & - & 0.73 & 0.000 \\ \hline Gray-Level Run Length Matrix (GLRLM) & Gray-Level Non-Uniformity & GLN_GLRLM & 0.7 & 0.001 \\ \hline \multicolumn{5}{c}{**Dynamic Parameters**} & **Symbol** & **AUC Values** & \(p\)**-Value** \\ \hline MAD of wash-in & WIN_MAD & 0.70 & 0.001 \\ \hline IQR of wash-in & WIN_IQR & 0.70 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of significant textural and dynamic features with the corresponding area under curve (AUC) and \(p\)-values. The best performance (ACC = 0.78; SENS = 0.68; SPEC = 0.88; PPV = 0.84; NPV = 0.74; AUC = 0.78) using textural features was reached with all 48 metrics and an LDA trained with balanced data (with ADASYN function). The best performance (ACC = 0.75; SENS = 0.80; SPEC = 0.72; PPV = 0.74; NPV = 0.79; AUC = 0.80) using morphological features was reached with an SVM trained with balanced data (with ADASYN function) and a subset of five robust morphological features. The subset of five robust morphological features included circularity (similarity of the lesion shape to a sphere), rectangularity (similarity of the lesion shape to a rectangle), sphericity (ratio between the average radial length and the standard deviation of the rays), gleaning (standard deviation of the radial lengths with respect to the radial length average) and surface (number of voxels belonging to the lesion boundary). Figure 3: Boxplots of significant textural and dynamic features with AUC values.