## 4 Discussion
For the KNN classifier (MRMRR selection, original dataset), the best configuration included 13 features, many of which were texture-based, including original_glcm_MCC,original_glcm_Correlation, original_glcm_ClusterShade, and original_ngtdm_Coarseness, alongside shape descriptors like original_shape_SurfaceVolumeRatio, original_shape_Flatness, and original_shape_LeastAxisLength. This suggests that KNN leveraged both morphological and microtextural heterogeneity of the lesion to achieve robust classification. The XGBoost model trained on the original dataset with Mutual Information selection retained only four features: patient age, original_shape_Elongation, original_shape_Flatness, and original_shape_LeastAxisLength, highlighting the model's reliance on shape descriptors and the clinical variable age, which may implicitly reflect biological aggressiveness and risk profiles. The SVM classifier (MRMR selection, with augmentation) achieved an AUC of 0.80 using only three features: original_glcm_Correlation, original_glrm_RunVariance, and original_shape_Elongation. This minimalistic set suggests that a combination of correlation-based texture and elongation-related morphology can be sufficient for accurate classification. Similarly, XGBoost with MRMR selection and augmentation achieved high performance (AUC = 0.79) using a 6-feature set, which included both texture (e.g., original_glcm_Correlation, original_glrm_RunVariance, original_glcm_ClusterShade, original_glcm_MCC) and shape descriptors (original_shape_Elongation), again underlining the relevance of both radiomic domains. Finally, the RF classifier (with augmentation and Mutual Information selection) relied solely on three shape-related features: original_shape_Elongation, original_shape_Flatness, and original_shape_LeastAxisLength. This indicates that, for tree-based methods, morphological geometry may carry the strongest discriminative signal in this dataset. Across models, a set of radiomic descriptors was consistently prioritised across different ML algorithms. These recurring features suggest robust discriminatory potential for distinguishing between in situ and invasive breast lesions, regardless of the specific classifier architecture or validation strategy. Among the most frequently selected features, original_shape_Elongation appeared in four out of five models (XGBoost with and without augmentation, RF, and SVM). This metric captures the deviation of a lesion from a spherical form and may reflect infiltrative growth patterns characteristic of invasive cancers. Similarly, original_glcm_Correlation, original_shape_Flatness, and original_shape_LeastAxisLength were each included in three models. These features describe different aspects of lesion morphology and internal texture organisation. GLCM Correlation quantifies the linear dependency of grey-level intensities in neighbouring pixels, potentially reflecting architectural complexity. Flatness and Least Axis Length are shape descriptors that provide insights into tumour geometry and spatial extension. Other features like original_glcm_MCC, original_glcm_ClusterShade, and original_glrm_RunVariance were selected in two models, confirming their secondary but notable contribution to classification. These texture metrics capture variations in intensity patterns and asymmetry within the lesion, both of which are associated with tumour heterogeneity and invasive potential. The consistency of these features across models suggests that they capture systematic imaging differences between in situ and invasive tumours, likely related to structural and morphological heterogeneity observable on MRI. Although radiomic features are mathematical descriptors without direct biological interpretation, their recurrence across independent algorithms reinforces their potential value as robust imaging biomarkers. This consistency also supports the interpretability and possible generalizability of the models developed in this study, warranting further validation in larger and more diverse cohorts.