Breast multiparametric ultrasound: a single-center experience.

PURPOSE: To evaluate the role of multiparametric ultrasound (mpUS) in the characterization of focal breast lesions (FBLs). METHODS: This prospective study enrolled patients undergoing multiparametric breast ultrasound for FBLs. An experienced breast radiologist evaluated the following ultrasound features: US BI-RADS category, vascularization pattern (internal, vessels in rim and combined) and presence of penetrating vessels with each Doppler method (Color-Doppler, Power-Doppler, Microvascular imaging), strain ratio (SR) and Tsukuba score (TS) with Strain Elastography (SE), Emax, Emean, Emin and Eratio with 2D-shear wave elastography (2D-SWE). Core biopsy for all BI-RADS 4-5 FBLs and 24-month follow-up for all BI-RADS 2-3 FBLs were considered for standard of reference. The diagnostic performance was assessed with the area under curve (AUCs) and cut-off values were determined according to the Youden's index. RESULTS: A total of 139 FBLs were included with 75/139 (53.9%) benign and 64/139 (46.1%) malignant FBLs. Internal vascularization patterns (p < 0.001), penetrating vessels (p < 0.001), TS 4-5 (p < 0.001) and all 2D-SWE parameters (p < 0.001) were significantly different between benign and malignant FBLs. The BI-RADS score provided an AUC of 0.876 (95% CI 0.810-0.926) for the diagnosis of malignant FBLs. Among the 2D-SWE measurements, an excellent diagnostic performance was observed for Emax with an AUC of 0.915 (95% CI 0.856-0.956) and Emean of 0.908 (95% CI 0.847-0.951). Optimal cutoff for the diagnosis of malignant FBLs were US BI-RADS > 3, Strain Ratio > 2.52, Tsukuba Score > 3, Emax > 82.6 kPa, Emean > 66.0 kPa, Emin > 54.4 kPa and Eratio > 330.8. Multiparametric ultrasound, particularly SWE, can improve specificity in the characterization of FBLs.

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.

PURPOSE: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs). MATERIAL AND METHODS: Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B). RESULTS: A total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively. CONCLUSION: AI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers.

MRI Insights in Breast Imaging.

In the world, breast cancer is the most commonly diagnosed cancer among women. Currently, MRI is the most sensitive breast imaging method for detecting breast cancer, although false positive rates are still an issue. To date, the accuracy of breast MRI is widely recognized across various clinical scenarios, in particular, staging of known cancer, screening for breast cancer in high-risk women, and evaluation of response to neoadjuvant chemotherapy. Since technical development and further clinical indications have expanded over recent years, dedicated breast radiologists need to constantly update their knowledge and expertise to remain confident and maintain high levels of diagnostic performance in breast MRI. This review aims to detail current and future applications of breast MRI, from technological requirements and advances to new multiparametric and abbreviated protocols, and ultrafast imaging, as well as current and future indications.

Diagnostic performance of 2D-shear wave elastography in the diagnosis of breast cancer: a clinical appraisal of cutoff values.

PURPOSE: To assess the role of 2D-shear wave elastography (2D-SWE) in differentiating benign from malignant focal breast lesions (FBLs), providing new vendor-specific cutoff values. METHODS: 158 FBLs (size: 3.5-50 mm) detected in 151 women (age: 21-87 years) were prospectively evaluated by means 2D-SWE. For each lesion, an expert radiologist assessed US BI-RADS category and calculated the following four 2D-SWE parameters: (1) elasticity maximum (Emax); (2) mean elasticity (Emean); (3) minimum elasticity (Emin); (4) elasticity ratio (Eratio). US-guided core-biopsy was considered as standard of reference for all the FBLs classified as BI-RADS 4 or 5. For each 2D-SWE parameter, the optimal cutoff value for a diagnostic test was calculated using the Youden method. Diagnostic performance of the US BI-RADS and 2D-SWE parameters was calculated accordingly. RESULTS: 83/158 (52.5%) FBLs were benign and 75/158 (47.5%) were malignant. Statistically significant higher stiffness values were observed in malignant FBLs for all 2D-SWE parameters than in benign ones (p < 0.001). 2D-SWE cutoff values were 82.6 kPa, 66.0 kPa and 53.6 kPa, respectively, for Emax, Emean, Emin and 330.8% for Eratio. The 2D-SWE parameter showing the best diagnostic accuracy was Emax (85.44%). Considering US BI-RADS 3 (n = 60) and 4a (n = 32) FBLs, Emax and Emean showed the best diagnostic accuracy (85.87% for both), without a statistically significant decrease in sensitivity (p = 0.7003 and p = 1, respectively). CONCLUSION: Our study provides new vendor-specific cutoff values for 2D-SWE, suggesting its possible clinical use in the adjunctive assessment of category US-BI-RADS 3 and 4a breast masses.