## 4 Discussion  
The challenge of distinguishing between invasive and in situ BC has been addressed in a few studies, although it remains an open and evolving area of research. In this context, our study investigated the performance of several ML algorithms combined with radiomic features extracted from manually segmented ROIs on post-contrast DCE-MRI. Among all classifiers, the highest performing models were SVM, KNN, XGBoost, and RF. While SVM and RF favoured higher precision and lower false-positive rates, KNN and XGBoost showed greater sensitivity toward in situ lesions. In particular, the best-performing model was the Random Forest classifier, which, when trained on the augmented dataset using 3 features selected through mutual information, achieved the highest AUC of 0.81 and an accuracy of 79%. These results demonstrate that traditional ML approaches, when properly optimised and validated, can yield robust performance even on relatively small datasets. These findings are in line with prior work exploring the potential of Radiomics in the classification of BC subtypes. For instance, in one study, radiomic features extracted from DCE-MRI in a large cohort (190 IDC and 58 DCIS cases) were used to train an RF classifier under LOOCV, achieving a remarkably high AUC of 0.90 [28]. Similarly, [PERSON] et al. tested a radiomic signature composed of 569 features derived from mammographic images, applied to a dataset of 161 DCIS and 89 IDC cases, obtaining a maximum AUC of 0.72 [29]. Alternative imaging modalities have also been explored. Diffusion-weighted imaging (DWI) has been used to compute apparent diffusion coefficient (ADC) values, which reflect tissue diffusivity and microstructure. Since invasive tumours typically alter the extracellular matrix through proteolytic activity, lower ADC values are observed compared to in situ lesions [30; 31]. One study confirmed this trend by demonstrating a statistically significant difference in ADC between 21 DCIS and 155 IDC cases (\(p<0.001\)), with a resulting AUC of 0.89 [32]. In another approach, [PERSON] et al. combined kinetic and morphological MRI features to train a classifier on a dataset comprising 32 benign, 71 DCIS, and 150 IDC cases, achieving an AUC of 0.83 [33]. More recently, deep learning (DL) techniques have also been tested. In one study, a transfer learning strategy using a pre-trained GoogleNet was employed to extract features from 131 breast MRI scans, followed by classification with an SVM, resulting in an AUC of 0.70 [34]. Compared to similar studies, our approach yielded competitive results despite the limited dataset size. For instance, [PERSON] et al. [28] reported an AUC of 0.90 using a large cohort and Random Forest classifier under LOOCV, while [PERSON] et al. [29] obtained an AUC of 0.72 with mammographic radiomics. [PERSON] et al. [33] combined kinetic and morphological features, achieving an AUC of 0.83, and [PERSON] et al. [34] reported an AUC of 0.70 using deep learning. Our best model, a Random Forest trained on three shape-based features, reached an AUC of 0.81, demonstrating that a carefully designed classical ML pipeline with robust feature selection can achieve results comparable to more complex or data-hungry methods. These findings confirm that radiomics-based ML approaches hold promise even in small, heterogeneous datasets when supported by appropriate preprocessing and validation strategies. To provide additional interpretability of our ML models, we analysed the specific features selected in the best-performing configurations for each classifier. The selected feature subsets reveal both recurring and unique variables, suggesting that different algorithms may prioritise distinct aspects of tumour morphology and texture in the classification of in situ versus invasive lesions.