## 1 Introduction  
Breast cancer (BC) is one of the most common malignant tumors as well as the leading cause of mortality among women worldwide. In Italy, BC affected about 53,000 new cases out of a total of 175,000 cases of all female cancers in 2019 [1]. BC can be classified into two main types--in situ and invasive. Based on cytological characteristics and growth patterns, the in situ type is further subdivided into ductal and lobular, located within the ductal or lobular epithelium, respectively. Ductal carcinoma in situ (DCIS) is more common than lobular carcinoma in situ (LCIS), accounting for 30-50% of all detected BCs [2; 3], and normally does not infiltrate through the basal membrane. On the other hand, the invasive ductal carcinoma (IDC) is the most common malignant lesion, accounting for approximately 70% of all malignant cases [4; 5]. Treatment choice is different between in situ and IDC (e.g., for the latter, the identification of the sentinel lymph node is required, which is not for the former, see the \"National Comprehensive Cancer Network Guidelines--Breast Cancer\" at [[https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf](https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf)]([https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf](https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf))). Moreover, clinical outcomes are worse for the invasive disease. Consequently, women might need to undergo additional surgical options if an invasive disease is missed. In the current clinical imaging practice, the use of Magnetic Resonance Imaging (MRI) has a high sensitivity and strongly improves tumor mass detection and discrimination between benign and malignant lesions [6; 7; 8; 9; 10]. Naturally, breast MRI scans must be interpreted by experienced radiologists, as these examinations are often used to improve the outcome of the surgical practice, by reducing the number of re-excisions, allowing patient selection for neoadjuvant chemotherapy or therapy modification, as well as representing a technique of choice for pre-surgical assessment of residual tumor size, to determine breast conservation surgery candidacy [6]. In this scenario, a new field of research called Radiomics is becoming increasingly popular, with the general aim being a conversion of all information contained in digital medical images into quantifiable features. The latter are normally related to tumor size, shape, pixel intensity, and texture associated with clinical outcomes and prognosis, defining a proper tumor Radiomics signature [11]. The usage of Radiomics signatures can lead to a remarkable improvement of detection rate [12; 13]. Starting from the above considerations, the aim of this study was to develop a software system that is able to differentiate in situ infiltration of BT in dynamic contrast-enhanced (DCE-MRI) images, based on lesion Radiomics signature. Preliminary results of this work, on a smaller dataset, with a partially different approach, and without the segmentation step, are reported in [14]. The problem of distinguishing invasive from in situ BC is debated in a few papers in the specific literature. In [15], Radiomics features are extracted from DCE-MRI scans (190 IDC and 58 DCIS) and are used to train a random forest classifier in a leave-one-out cross-validation scheme. AUC of the ROC curve was 0.90. A Radiomics signature of 569 features was tested by [PERSON] et al. [16] in mammographic images; the dataset was composed of 161 DCIS and 89 IDC and their best result was AUC = 0.72. In [17], the apparent diffusion coefficient (ADC) computed from diffusion-weighted MRI (DWI) was used to distinguish invasive from in situ DCIS. DWI characterizes tissue diffusivity, therefore it provides a description of tissue micro-structure [18; 19].