###### Abstract  
Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test-retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%. radiomic; digital breast tomosynthesis; calcifications; diagnostic imaging +
Footnote †: journal: journal: Medical Bioimaging and Physiology, National Research Council, Via [PERSON] 93, 2009 Segre, Milan, Italy
However, accurate visualization of calcifications and discrimination between benign and malignant ones remains an issue for human readers even with DBT. Impressions from initial studies on DBT in the 1990s supposed lower accuracy compared to standard digital mammography (DM), mainly because a cluster of microcalcifications may be visible on different two-dimensional (2D) images, with poor resolution in out-of-focus images and lack of comprehensive cluster visualization [5]. These drawbacks have been overcome using DBT image series and/or DBT-derived synthetic two-dimensional (2D) views, offering a visualization of calcifications similar or even highlighted when compared to standard DM. Notwithstanding these improvements, the malignancy rate of the calcifications addressed to needle biopsy on the basis of DBT remained relatively low, as it was with 2D mammography, with a not-negligible amount of FP, as reported by [PERSON] et al. [6]. Radiomics is a relatively new image-analysis approach allowing the quantitative measurement of high-throughput features from radiological images that are supposed to express the heterogeneity of texture, shape and size of distinct tissue phenotypes correlated to different clinical outcomes [7]. When combined with machine learning algorithms, the radiomic approach was proven able to provide automatic computer-aided classifiers of radiological images. Indeed, radiomic analysis has already been widely implemented in the last few years in various clinical applications [8; 9; 10], showing promising results in clinical decision support systems (DSS) and fostering highly tailored medical decision-making in both diagnosis and prognosis [11; 12]. Radiomic analysis of DBT (as well as DM) images is supposed to have the intrinsic capability to capture and quantitatively measure those morphometric and textural heterogeneity features of calcifications invisible to the radiologists' eyes that can be associated with malignancy or poor prognosis, potentially addressing the above-mentioned shortcomings, thus boosting DBT diagnostic performance in women recalled for assessment after calcifications are detected at screening DM [3] or when using DBT as a screening tool, as recently allowed by guidelines issued by the European Commission Initiative on Breast Cancer [13].