## 4 Discussion  
The differential diagnosis of breast tumors is still a significant challenge despite new technological developments that have provided more helpful tools for the radiological distinction of cancer from benign lumps. The BI-RADS lexicon, updated in the last version of 2013 by the American College of Radiology (ACR), aims to standardize the interpretation and reporting of breast imaging, including ultrasound examination [7]. Despite the validation of this system, the variability in the analysis of focal breast lesions is still a problem, in particular, for some of its descriptors, such as margins and echo-patterns of the lesions. For this reason, automatic and semi-automatic software and computer-aided artificial intelligence have been created in recent years in order to process the images and extract features that can be classified according to the algorithms used in each analysis phase. According to some authors, these systems can potentially improve the classification of breast masses by increasing the radiologist's performance. In a study by [PERSON] et al., a computer-aided system correctly upgraded malignant lesions initially assigned a BI-RADS 3 score at mammography to a BI-RADS 4 or 5 category at 90% [16]. However, a 2019 review by [PERSON] et al. that evaluated the potential role of artificial intelligence for supporting breast imaging in mammography, ultrasound, and MRI, showed a variability of the results found in the literature, focusing on the need to carry out large-scale cohort studies in order to have more data and to standardize the interaction with the automatic tools [14]. [PERSON] et al. demonstrated that a computer-aided classification had a statistical improvement in the accuracy and specificity of US examination (5.91% and 8.85%, respectively) compared \begin{table}
\begin{tabular}{c c c c c c} \hline
**Operator** & **Sensitivity (\%)** & **Specificity (\%)** & **ROC Area** & **PPV** & **NPV** \\ \hline Experienced radiologist & 93.2\% & 75\% & 0.84 & 87.2\% & 85.7\% \\ \hline “SmartBreast” & 78.8\% & 70.8\% & 0.81 & 85.4\% & 81\% \\ \hline Strain Ratio (SR) & 81\% & 86\% & 0.84 & 84.2\% & 80\% \\ (cut-off = 1.69) & 81\% & 86\% & 0.84 & 84.2\% & 80\% \\ \hline
2D SWE & 73\% & 55\% & 0.64 & 78.9\% & 57.4\% \\ (cut-off = 25 kPa) & 73\% & 55\% & 0.61 & 71.1\% & 58.6\% \\ \hline pSWE (cut-off = 64.34 kPa) & 71\% & 50\% & 0.61 & 71.1\% & 58.6\% \\ \hline SR + Experienced radiologist & 94.5\% & 78.8\% & 0.80 & 85.5\% & 96.4\% \\ \hline SR + & 79.3\% & 76.6\% & 0.82 & 84.7\% & 81.2\% \\ “SmartBreast” & 98.8\% & 61.7\% & 0.83 & 82\% & 96.7\% \\ \hline
2 DSWE + Experienced radiologist & 93.4\% & 75.5\% & 0.67 & 79.5\% & 85.8\% \\ \hline pSWE + & 78\% & 62.3\% & 0.71 & 79.7\% & 66.4\% \\ “SmartBreast” & 88.6\% & 64.5\% & 0.66 & 78.3\% & 79.7\% \\ \hline pSWE + Experienced Radiologist & 92.9\% & 68.9\% & 0.65 & 78.4\% & 68.3\% \\ \hline
2D SWE + & 75.6\% & 68.7\% & 0.55 & 77.8\% & 67.6\% \\ \hline pSWE+ & 93.1\% & 71.8\% & 0.73 & 78.8\% & 69.4\% \\ “SmartBreast & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of methods and their combination. PPV—positive predictive value, NPV—negative predictive value, ROC area—receiver operating characteristic area. to a radiologist alone [17]. In 2017, [PERSON] et al. reported a higher and statistically significant AUC (0.725) of a series of 192 breast lesions assessed using a computer-aided system called S-Detect in comparison with an expert radiologist (0.653) (\(p\) = 0.038) [18]. [PERSON] et al. showed, in their study, that this software can lead to an increase in the number of breast masses correctly characterized by US, although this increase was not statistically significant [19]. A 2021 meta-analysis by [PERSON] et al. indicated how the evaluation with the same type of CAD (S-Detect) could play a role in the distinction between benign and malignant lesions with high values of sensitivity and specificity (0.82 and 0.83, respectively) [20]. [PERSON] et al.