## 2 Methods
### Image Processing  
Regions of interest (ROIs) were manually drawn slice-by-slice by two expert radiologists with 22 and 15 years of breast imaging experience, respectively; the segmentation was performed by the two radiologists first separately and then together and in accordance with each other, annotating all slices of the lesions. The margins of the breast lesions were defined on the third T1-weighted subtracted series of DCE-MRI, where contrast uptake was emphasized. Then, the mask obtained by the segmentation was used to obtain the volume of interest for each of the 9 series of DCE-MRI examination. An example is shown in Figure 1.  
For each volume of interest, textural, morphological and dynamic features were extracted by the volumes of interest obtained by the consensus of two radiologists. Radiomic textural, morphological and dynamic features were also extracted considering the volumes of interest obtained separately by the two radiologists. All T1-weighted sequences of each patient were processed in MATLAB (The MathWorks, Inc., Natik, MA, USA) [56]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Benign (35 Lesions)** & **Number** & **Percentage Value (\%)** \\ \hline Fibrosis & 5 & 14.29 \\ Ductal hyperplasia & 14 & 40.00 \\ Fibroadenoma & 10 & 28.57 \\ Dysplasia & 2 & 5.71 \\ Adenosis & 3 & 8.57 \\ Other & 1 & 2.86 \\ \hline
**Malignant (56 Lesions)** & **Number** & **Percentage Value (\%)** \\ \hline Infiltrating lobular carcinoma & 14 & 25.00 \\ Infiltrating ductal carcinoma & 13 & 23.21 \\ Ductal carcinoma in situ & 24 & 42.86 \\ Intraduct papilloma & 2 & 3.57 \\ Tubular carcinoma & 1 & 1.79 \\ Papillary carcinoma & 2 & 3.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number and corresponding percentage of the total benign or malignant breast lesions. Figure 1: Segmentation (in red) on a single image of the third T1-weighted subtracted series: infiltrating ductal carcinoma in a 39-year-old woman. The Texture Toolbox of MATLAB(r), which includes 48 parameters and performs texture analysis from a 2D or 3D input, was considered [57]; the textural features of this Toolbox were calculated according to the Image Biomarker Standardization Initiative [58]. The textural features include both first-order features and second-order ones. The Texture Toolbox package implements wavelet band-pass filtering, isotropic resampling, discretization length corrections and different quantization tools. The toolbox can be downloaded at [[https://it.mathworks.com/matlabcentral/fileexchange/51948-radiomics](https://it.mathworks.com/matlabcentral/fileexchange/51948-radiomics)]([https://it.mathworks.com/matlabcentral/fileexchange/51948-radiomics](https://it.mathworks.com/matlabcentral/fileexchange/51948-radiomics)) (accessed on 15 April 2019). A detailed description for each extracted textural feature has been provided in Supplementary Materials. Morphological features describe and quantify the shape and structure of segmented volumes of interest. Malignant lesions may have very particular patterns of shape and specific irregularities on the borders. We considered a feature set including 15 morphological features including radial length average, entropy of radial length, irregularity, diameter, circularity, compactness, smoothness, roughness, sphericity, volume, rectangularity, surface, convexity, gleaning and curvature. A detailed description of the morphological features has been provided in Supplementary Materials. Dynamic features measured by a time-intensity curve (TIC) describe the signal intensity, reflect tumor micro-vascularity characteristics and, in general, will tend to increase more in malignant cancer tissues than in healthy ones or less aggressive types of tumors [59; 60]. Several features belonging to the dynamic category have been proposed so far in the literature [59; 60] among them, 9 features were extracted voxel-by-voxel from volumes of interest using a semi-quantitative approach.