Innovative integration of automated breast volume scan and ultrasound elastography for enhanced differentiation of benign and malignant breast lesions.

Breast cancer remains the most common malignancy among women and a leading cause of cancer-related mortality worldwide. Early detection through imaging is critical for improving patient prognosis. While conventional two-dimensional ultrasound (2D-US) is widely utilized in clinical practice, it has several limitations, including high operator dependency, limited reproducibility, and the lack of standardized examination protocols. To overcome these challenges, the Automated Breast Volume Scan (ABVS) has been developed as an advanced ultrasound technique, offering fully automated, high-resolution breast imaging with improved reproducibility and standardized acquisition. Furthermore, ultrasound elastography (UE) provides complementary functional information by assessing tissue stiffness, aiding in the differentiation between benign and malignant breast lesions. This study aimed to assess the diagnostic performance of ABVS combined with UE in distinguishing benign from malignant breast lesions. A prospective study was conducted involving 93 patients with a total of 103 breast lesions, all classified as Breast Imaging-Reporting and Data System (BI-RADS) category 4 or higher on conventional ultrasound. Between October 2023 and October 2024, all participants underwent both ABVS and UE, with pathological biopsy results serving as the diagnostic "gold standard". Of the 103 lesions, 47 were confirmed malignant, while 56 were benign. The combined diagnostic approach yielded a sensitivity of 91%, specificity of 75%, and overall accuracy of 84%, demonstrating superior diagnostic performance compared to either ABVS or UE alone. Our findings indicate that the integration of ABVS and UE enhances diagnostic accuracy, improves lesion localization, reduces operator dependency, and strengthens the reliability of breast cancer detection. This combined imaging strategy holds significant potential for optimizing breast cancer screening and early diagnosis, ultimately contributing to better clinical outcomes.