**1. Introduction**

In the screening, detection and follow-up of breast cancer, the mammography (MX) was considered the first imaging examination [1,2]. In particular, thanks to the technological improvements achieved by combining digital mammography with techniques that allow low and high energy images to be obtained, and with the administration of iodate contrast agent, it is possible to acquire images that emphasize the vascularity linked to malignant lesions by the contrast agen<sup>t</sup> enhancement. This imaging technique is recognized as contrast-enhanced mammography and exploits the same physiological mechanisms as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

DCE-MRI is an important complementary diagnostic imaging technique that was validated in the screening of high-risk women and dense breasts and in the monitoring of oncological therapies, thanks to its capability of combining morphological and functional information [2,3].

Previous studies have evaluated the sensitivity of CEM compared to conventional digital MX, ultrasound (US) and MRI [4–8]. CEM sensitivity has been reported in the range of 90–100% [5–7], which is significantly higher than the sensitivity of MX or US alone [4–7]. Moreover, CEM allows for the identification of additional occult cancers via mammography to more accurately assess the disease extent, and to guide surgical and treatment planning [8–12].

Radiomics and artificial intelligence approaches have been extensively applied to process both CEM and DCE-MRI in order to increase diagnostic performance in the detection of malignant breast lesions [13,14]. By means of the radiomics approach, it is possible to obtain, from medical images, a large amount of quantitative data that, combined with pattern recognition procedures, allow for the resolution of many clinical issues with high accuracy. Examples of features used in the oncology field are tumor size and shape, as well as intensity, statistical and textural metrics [15–42].

In this study, we designed several classifiers with the aim of discriminating between benign and malignant breast lesions using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed.
