## 1 Introduction  
Breast cancer (BC) remains one of the most prevalent malignancies affecting women worldwide. In Italy alone, approximately 53,686 new cases and 15,500 deaths were reported in 2024 [1].  
Malignant breast tumours can be classified as either lobular or ductal, depending on whether they originate in the lobules or the ducts of the breast [2; 3]. These carcinomas may remain confined to their site of origin, in which case they are referred to as in situ, or they may spread to the surrounding tissues, in which case they are considered invasive. In situ lesions are characterised by malignant cells that respect the integrity of the basement membrane and do not penetrate the surrounding stroma. By contrast, invasive disease is defined by tumour cells that breach this membrane and infiltrate adjacent connective and adipose tissues [4; 5].  
The clinical management pathways for in situ versus invasive lesions differ significantly; for instance, sentinel lymph node biopsy, which is routinely recommended for invasive lesions, is generally not indicated in the treatment of in situ lesions. Moreover, invasive tumours are associated with a poorer prognosis, higher risk of metastasis, and increased likelihood of recurrence [6; 7; 8; 9; 10]. Consequently, misclassification of an invasive lesion as in situ could result in under-treatment, leading to inadequate surgical planning and compromised clinical outcomes. In this context, accurate preoperative differentiation between in situ and invasive breast lesions is crucial for optimising treatment decisions and improving patient care.  
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has emerged as a key imaging modality for breast cancer detection, staging, and characterisation, particularly in women with dense breast tissue or high familial risk [11; 12; 13; 14; 15]. Its superior sensitivity compared to other imaging techniques makes it an invaluable tool in identifying suspicious lesions. MRI is commonly employed to optimise surgical outcomes by minimising the likelihood of re-excisions, assisting in the identification of patients suitable for neoadjuvant chemotherapy or adjustments in therapeutic strategies. Moreover, it serves as a preferred modality for the preoperative evaluation of residual tumour burden, helping to assess eligibility for breast-conserving surgery [11]. However, interpreting breast MRI scans requires considerable expertise, as these examinations play a pivotal role in informing surgical decision-making. Despite its high diagnostic potential, conventional radiological evaluation of DCE-MRI is still largely qualitative and subject to inter-observer variability, which can limit its reliability in accurately distinguishing between in situ and invasive lesions. In this context, quantitative imaging approaches, named Radiomics, have garnered increasing interest [16]. Radiomics enables the extraction of a large number of features from medical images, offering a non-invasive window into tumour phenotype, heterogeneity, and underlying biology [17; 18]. When combined with Artificial Intelligence (AI) and Machine Learning (ML) techniques, Radiomics has the potential to enhance diagnostic precision, improve lesion classification, and support clinical decision-making in breast cancer care [19; 20; 21]. Several studies have demonstrated that ML models trained on radiomic features extracted from DCE-MRI can outperform conventional assessment in distinguishing malignant subtypes [22; 23; 24; 25; 26].  
In this study, we developed and evaluated an ML framework for the binary classification of breast tumours as either in situ or invasive, based on radiomic and clinical features extracted from a region of interest (ROI) containing the tumour tissue, in post-contrast DCE-MRI images. Our final aim was to investigate whether a Radiomics-based AI approach can aid in the pre-operative classification of breast lesions, potentially contributing to more personalised and effective patient management.