## 4 Discussion
evaluated the role of S-Detect together with SWE, demonstrating that the differences in sensitivity, specificity, accuracy, and AUC of the three diagnostic methods were not statistically significant (all \(p\) > 0, 05), however, encouraging this technology as a tool in supporting young and less experienced radiologists in breast imaging evaluation [21]. Similar results were also reported in the previous work by [PERSON] et al. [22]. A 2022 study by [PERSON] et al. showed that automatic ultrasound detection of BI-RADS 4 lesions performed by artificial intelligence had higher accuracy in determining benign and malignant tumors (0.87, 0.75, and 0.80, respectively, for sensitivity, specificity, and accuracy) than conventional ultrasound (0.85, 0.67, and 0.75, respectively) [23]. It is noteworthy that, to date, there are no papers in the literature that evaluate the role of the new \"SmartBreast\" in the discrimination of the individual BI-RADS lexicon descriptors or the combined role of the software together with the different elastography techniques in the assessment of the final BI-RADS score. The results of our study showed a moderate agreement between the \"SmartBreast\" software and the human investigator in the evaluation of tumor dimensions ([PERSON]'s coefficient = 0.90, \(p\) = 0.07). A good agreement was found in the identification of the posterior features and in the evaluation of the shape and the analysis of vascular signals at color Doppler ([PERSON]'s k of 0.94, 0.6, and 0.65, respectively). The agreement was found to be lower in the analysis of the most difficult BI-RADS parameters, such as orientation, margins, and echo pattern of the breast masses ([PERSON]'s k of 0.28, 0.33, and 0.48, respectively). With regard to the identification of microcalcifications, the area under the ROC curve was equal to 0.64, despite a [PERSON]'s k value of 0.36, demonstrating a good agreement between the \"SmartBreast\" the human operator, and mammography. The agreement in the definition of the BI-RADS final score was poor between software and operator (0.35) and between software and histological results (0.28), but it was good between operator and histological final assessment (0.71). These data confirm the potential role of CAD systems in supporting breast lesion characterization for less experienced operators, but this system is neither of better diagnostic performance nor can it currently replace a radiologist with experience in breast cancer imaging. One of the limits found in our study that can partly explain these results is the presence of nine small lesions (9/92, 9.7%) \(\leq\) 6 mm in size, for some of which the \"SmartBreast\" software was not able to correctly identify the lesion alone, or it did not correctly define the margins in consideration of the exceedingly small size. Some studies have also evaluated the diagnostic role of elastography in breast nodule discrimination. In particular, some authors suggested that strain elastography may be complementary to the BI-RADS classification, increasing the performance of ultrasound [24; 25]. [PERSON] et al. [26] reported sensitivity values of 0.94 and specificity of 0.87 with a cut-off of 2.9 for SR. On the other hand, [PERSON] et al. [27] reported a cut-off value of 5.6 associated with the best diagnostic performance of SR. Therefore, it is evident how much the SR cut-off values are variable, and this variability is related to different equipment used, to operators' experience, and to the lesions' depth and size. A further method, shear wave elastography (SWE), has been applied in breast lesion ultrasound analysis. A 2022 study by [PERSON] et al. showed that the use of combined qualitative 2D SWE and/or color Doppler with BI-RADS had higher AUC values (\(p\) < 0.001) than BI-RADS alone and that, compared with BI-RADS alone, the sensitivity slightly decreased, while the specificity, PPV, NPV, and accuracy were significantly improved [28]. [PERSON] et al.