Comparison of the Effectiveness of Shear Wave Elastography and Superb Microvascular Imaging in the Evaluation of Breast Masses

This study aims to determine the diagnostic performance of superb microvascular imaging (SMI) and shear wave elastography methods in evaluating breast lesions. We will also compare the effectiveness of the stiffness, velocity, and vascular index (VI) parameters in distinguishing malignancy. From January to June 2019, 121 patients with 121 solid breast masses (category 4 and 5 lesions according to the Breast Imaging-Reporting and Data System) detected during the routine grayscale sonographic examination were included in the study. Stiffness and velocity values were obtained using shear wave elastography for all lesions, and VI was obtained using SMI. The receiver operating characteristic curves were obtained to set the best cutoff values for the stiffness, velocity, and VI to differentiate patients with malignant breast lesions. All 121 lesions were pathologically verified by US-guided core needle biopsy. Forty-seven (38.9%) of the lesions were malignant, and 74 (61.1%) were benign. Median stiffness, velocity, and VI values were significantly lower in benign masses compared with malignant masses (P < 0.001). The optimum cutoff values for the stiffness, velocity, and VI were determined to be 58.3 kPa, 4.5 m/s, and 1.1%, respectively. The areas under the curves were 0.897 for stiffness, 0.884 for velocity, and 0.687 for VI. Shear wave elastography and SMI are noninvasive methods that may be used to evaluate breast masses. Although both methods' quantitative data are beneficial in differentiating malignant from benign masses, stiffness is the best parameter to be used.

Automated summary

This study aimed to determine the diagnostic performance of superb microvascular imaging (SMI) and shear wave elastography in evaluating breast lesions. Stiffness was found to be the best parameter for distinguishing malignant from benign masses, although both SMI and shear wave elastography provided valuable quantitative data for differentiation.