## 2 Materials and Methods
### Radiomic Classifier  
Radiomic methodology was applied to include DBT images of patients, according to the International Biomarker Standardization Initiative (IBSI) guidelines [22]. For this purpose, the TRACE4(r) radiomic platform was used [23], allowing the whole IBSI-compliant radiomic workflow combined with ensembles of machine learning (ML) classifiers to be obtained in a fully automatic way. IBSI radiomic workflow included:  
(i) the segmentation of the calcification region from each patient DBT image,  
(ii) the preprocessing of image content within the segmented region of interest for the radiomic feature extraction,  
(iii) the extraction of radiomic features from the segmented region of interest,  
(iv) the selection of radiomic features stable with respect to different segmentations (it may occur that different human operators segment the calcification regions of different images) and repeatable in test-retest study,  
(v) the use of such stable radiomic features to train, validate, and test different ensembles of ML classifiers in the binary classification task of interest (malignant vs. benign), including the reduction of such stable and repeatable features to not-redundant features in a number that is statistically proper to the number of included image samples of patients. More specifically:  
The segmentation of the calcification region was performed manually, slice by slice, by the expert radiologist, using the TRACE4 Segmentation tool. The preprocessing of image intensities within the segmented region of interest included resampling to isotropic voxel spacing, using a down-sampling scheme by considering image slice thickness of 1 mm and intensity discretization using a fixed number of 64 bins. The radiomic features extracted from the segmented region of interest belong to different families: morphology, intensity-based statistics, intensity histogram, gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighborhood gray tone difference matrix (NGTDM), gray-level distance zone matrix (GLDZM), and neighboring gray level dependence matrix (NGLDM). Their definition, computation and nomenclature are compliant with the IBSI guidelines, except for the features of the family morphology, originally designed for 3D images, which were replaced with ten 2D equivalent features (e.g., 3D features volume and surface were replaced with 2D features area and perimeter, respectively). Additional details:  
* Morphology (10 features). Morphological features (such as area) describe the geometry of an ROI and are based on the voxel contained in the analyzed ROI. For this feature class, IBSI guidelines could not be entirely followed, since 3D images are required while mammography images are 2D images by definition. * Intensity-based statistics (18 features). Intensity-based statistical features (such as mean intensity, median intensity and intensity variance) describe the intensity distribution within the ROI. * Intensity histogram (22 features). To calculate intensity histogram features (such as mean discretized intensity, discretized intensity variance and discretized intensity kurtosis), the original intensity distribution is discretized into intensity bins. * Grey level co-occurrence matrix (100 features). These features are second-order features describing image texture according to pixels' distribution. For this purpose, a specific matrix was designed to represent in each element the number of times two specific pixels are at a defined distance and angle. The given matrix was used to calculate features such as autocorrelation, cluster shade and cluster prominence [24; 25]. * Grey level run length matrix (63 features). These features derive from a support matrix representing the number of times specific pixel/voxel intensity is present in a given direction. Examples for this group of features are grey level non-uniformity, run-length non-uniformity and grey level variance [24; 25]. * Grey level size zone matrix (32 features).