## 3 Results
The green curve shows the Random Forest (RF) model trained on augmented data and using mutual information-selected features. The red curve refers to the Support Vector Machine (SVM) model trained on augmented data with MRMR feature selection. The purple curve illustrates the performance of the XGBoost model trained on augmented data with MRMR-selected features. The diagonal grey dashed line indicates the performance of a random classifier (AUC = 0.50). The KNN classifier, trained on the non-augmented dataset with 13 MRMR-selected features, reached an AUC of 0.78 and a best accuracy of 76%. Specifically, it achieved a recall of 42% and a precision of 77%, resulting in an F1-score of 54%. The XGBoost model, trained on the original dataset and evaluated using four features selected through mutual information, achieved an AUC of 0.77. At its optimal threshold, the model reached a best accuracy of 76%, precision of 73%, recall of 46%, and an F1-score of 56%. The RF classifier, trained on the augmented dataset with three features selected via mutual information, yielded the best performance among all models, with an AUC of 0.81. It achieved a best accuracy of 79%, precision of 76%, recall of 54%, and an F1-score of 63%. The SVM model, trained on the augmented dataset with the top 3 MRMR-selected features, achieved an AUC of 0.80. At the accuracy-maximising operating point, the model reached an accuracy of 79%, with a precision of 76%, a recall of 54%, and an F1-score of 63%. The XGBoost model, trained on the augmented dataset with 6 MRMR-selected features, reported an AUC of 0.79. It reached a best accuracy of 77%, precision of 67%, recall of 67%, and an F1-score of 67%. Table 2 summarises the radiomic and clinical features selected by the best-performing configurations of each ML classifier. The number of features retained by each model varied from 3 to 13, depending on the selection method and use of data augmentation. Notably, several features were selected across multiple classifiers, suggesting shared patterns of predictive relevance. For instance, shape-based descriptors such as Elongation, Flatness, and LeastAxisLength were frequently chosen by different models, as were texture-based metrics including GLCM Correlation, ClusterShade, MCC, and RunVariance. The recurrence of these features supports their potential role in capturing biologically meaningful differences between in situ and invasive lesions, as further discussed in the following section. To further support the feature selection process and assess potential redundancies among variables, a correlation matrix was computed over the union of all features selected by the different classifiers, as shown in Figure 5. This analysis allowed us to qualitatively verify the suitability of the selected subsets, especially in the context of redundancy-aware methods such as MRMR, by highlighting the presence or absence of strongly correlated feature pairs. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Augmentation**} & \multirow{2}{*}{\
\begin{tabular}{c} **Selection** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{**Selected Features**} & \multicolumn{1}{c}{**Number of Selected**} \\  & & & original\_glcm\_MCC, & \\  & & & original\_glrlm\_Run\_Percentage, & \\  & & & original\_ngtdm\_Coarseness, & \\  & & & original\_glcm\_ClusterShade, & \\  & & & original\_glcm\_Correlation, & \\  & & & original\_shape\_SurfaceVolumeRatio, & \\  & & & original\_glcm\_Idn, & \\  & & & original\_glszm\_ZoneEntropy, original\_shape\_Flatness, & \\  & & & original\_gldm\_DependenceEntropy, & \\  & & & original\_gldm\_DependenceVariance, & original\_gldm\_SmallDependenceLowGrayLevelEmphasis, \\  & & & original\_shape\_LeastAxisLength & \\ \hline XGBoost & No & Mutual Info &
\begin{tabular}{c} age, \\ original\_shape\_Elongation, \\ original\_shape\_Flatness, \\ original\_shape\_LeastAxisLength \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selected radiomic and clinical features for best-performing classifiers.