## 4 Discussion  
Radiomics derive multiple quantitative features from single or multiple medical imaging modalities and techniques, highlighting image traits that are not visible to the naked eye and hence potentially significantly augmenting the diagnostic and prognostic power of medical imaging. This quantitative \"big data\" approach is a relatively new discipline showing possible limitless applications in clinical practice and research [28; 29]. Radiomics strengths in oncology, however, until now have been frequently demonstrated for tomographic imaging, being inherently quantitative tools, where radiomics can provide a comprehensive noninvasive characterization of the whole 3D tumor, defining what has been named the radiomics signature of the tumor [7]. Among published studies, radiomics has been increasingly gaining ground to improve cancer diagnosis, monitoring of treatment response and prognosis, also in the field of breast care [30]. In this work, a radiomic classifier was developed for DBT images on suspected breast calcifications to predict associated malignancy at needle biopsy with the particular purpose to reduce FP of DBT. We analyzed a total of 49 breast calcifications detected at DBT as positive, classified by an expert radiologist according to BI-RADS, and ad \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**\# Patient**} & **DBT-Guided VAB and Follow-Up** & \multirow{2}{*}{**DSS Classification**} \\  & **(Reference Standard Classification)** & \\ \hline
21 & Benign (B1) & High risk of malignancy \\
22 & Malignant (B5b) & High risk of malignancy \\
23 & Malignant (B5a) & High risk of malignancy \\
24 & Malignant (B5a) & High risk of malignancy \\
25 & Benign (B2) & Low risk of malignancy \\
26 & Benign (B2) & High risk of malignancy \\
27 & Benign (B1) & Low risk of malignancy \\
28 & Benign (B2) & Low risk of malignancy \\
29 & Benign (B2) & High risk of malignancy \\
30 & Malignant (B5a) & High risk of malignancy \\
31 & B3 (Negative at follow up) & High risk of malignancy \\
32 & Malignant (B5a) & High risk of malignancy \\
33 & Benign (B3) & Low risk of malignancy \\
34 & Benign (B2) & High risk of malignancy \\
35 & Benign (B1) & Low risk of malignancy \\
36 & B3* (Negative at follow up) & Low risk of malignancy \\
37 & B3* (Negative at follow up) & Low risk of malignancy \\
38 & B3* (Negative at follow up) & High risk of malignancy \\
39 & Benign (B2) & High risk of malignancy \\
40 & Malignant (B5a) & High risk of malignancy \\
41 & Malignant (B5a) & High risk of malignancy \\
42 & Malignant (B5b) & High risk of malignancy \\
43 & Benign (B2) & High risk of malignancy \\
44 & B3 (Negative at follow up) & Low risk of malignancy \\
45 & B3* (Negative at follow up) & Low risk of malignancy \\
46 & Benign (B2) & High risk of malignancy \\
47 & Benign (B1) & Low risk of malignancy \\
48 & Malignant (B5a) & High risk of malignancy \\
49 & Benign (B2) & High risk of malignancy \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(Cont.\) \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Malignant at Reference** & **Benign or Negative at** \\  & **Standard** & **Reference Standard** \\ \hline
**High risk at DSS classification** & 18 TP & 15 FP \\
**Low risk at DSS classification** & 0 FN & 16 TN \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reference standard and DSS classification results of the tested patients. Number of true positive (TP) and false positive (FP). dressed to DBT-guided VAB with known histopathologic characterization and follow up at 1-2 years. We used these supervised associations to train an ensemble of ML classifiers to automatically distinguish malignant from benign cases from their DBT. Such a predictive radiomic classifier achieved an Accuracy, Sensitivity, Specificity, AUC, PPV and NPV of 0.82, 0.78, 0.85, 0.80, 0.74, and 0.88, respectively, according to a nested 10-fold cross-validation classification. A sensitivity of 78% is currently insufficient to avoid immediate referral to VAB only on the basis of our radiomic model.