# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
###### Abstract
Among these factors, breast density, which represents the amount of fibrous and glandular tissue in the breast, plays a fundamental role, as women with denser breast tissues can be exposed to a higher risk of developing breast cancer [6; 7]. However, it has been shown that the sensitivity of digital mammography is affected by the density of the breast tissue, with values ranging from 86 to 89% in largely fatty breasts and from 62 to 68% in extremely dense breasts. For this reason, the visual inspection of mammograms, despite processing to improve the quality of mammographic images [8; 9; 10], does not allow accurate discrimination between dense tissues and tumor lesions, which present similar properties since most tumors absorb X-rays similarly, to an extent, to fibroglandular tissues, as both appear white (dense) on mammograms [11; 12; 13]. Breast density represents the amount of fibroglandular tissue compared to fatty tissue and, on a mammography report, is assigned on a qualitative basis, through a visual evaluation, by the radiologists. The classification is based on the international standard BI-RADS (Breast Imaging--Reporting and Data System), based on which it is possible to identify four density classes [14]: (i) class A, predominantly fibro-adipose tissue; (ii) class B, scattered areas of fibroglandular tissue; (iii) class C, heterogeneously dense tissue; and (iv) class D, extremely dense tissue. To overcome the inter- and intra-observer variability to which visual evaluations are subject, in recent years, many methods of automatic detection on bio-images have been developed, through the extraction of radiomic features [15] combined with artificial intelligence (AI) techniques, for different clinical purposes [16; 17; 18; 19; 20; 21; 22]. In this study, the extracted features will represent the textural characteristics of the breast parenchyma [23; 24; 25]. Radiomics for the study of breast parenchyma is widely investigated in the literature not only to evaluate risk factors such as breast density but also for the early diagnosis of different types of breast cancer [26; 27; 28]. Furthermore, the analysis is extended to different imaging modalities [29], from magnetic resonance (MR) imaging [30] to computed tomography (CT) [17; 31] to ultrasound (US) [32] and digital tomosynthesis [33], but to date, the gold standard for the early diagnosis of breast cancer is considered digital mammography. The possibility of correlating the extracted features with breast density is then investigated. The first preliminary step, in Medio-Lateral-Oblique (MLO) images, is to segment the breast parenchyma with respect to the background but above all to the pectoral muscle, which can be interpreted as fibroglandular tissue, if not correctly segmented, leading to an overestimation of the breast density. Extending the results previously presented by the authors [23], this study is primarily intended to evaluate automatic segmentation methods to remove pectoral muscle from MLO mammographic images among those present in the scientific literature. Next, it is meant to test the capability of machine learning and neural networks in classifying breast density based on textural features extracted from only breast parenchyma. Therefore, the main novelties and advances compared to the previous work are (i) the evaluation of automatic segmentation methods to remove pectoral muscle from mediolateral oblique (MLO) mammographic images; (ii) the introduction of a multiclass classification based on BI-RADS guidelines; and (iii) the test of the capability of machine learning and neural networks in classifying breast density on imbalanced dataset.