Combined use of super-resolution ultrasound imaging and shear-wave elastography for differential diagnosis of breast masses.

OBJECTIVES

Shear-wave elastography (SWE) provides valuable stiffness within breast masses, making it a useful supplement to conventional ultrasound imaging. Super-resolution ultrasound (SRUS) imaging enhances microvascular visualization, aiding in the differential diagnosis of breast masses. Current clinical ultrasound diagnosis of breast cancer primarily relies on gray-scale ultrasound. The combined diagnostic potential of tissue stiffness and microvascular characteristics, two critical tumor biomarkers, remains insufficiently explored. This study aims to evaluate the correlation between the elastic modulus, assessed using SWE, and microvascular characteristics captured through SRUS, in order to evaluate the effectiveness of combining these techniques in distinguishing between benign and malignant breast masses.

MATERIALS AND METHODS

In this single-center prospective study, 97 patients underwent SWE to obtain parameters including maximum elasticity (Emax), minimum elasticity (Emin), mean elasticity (Emean), standard deviation of elasticity (Esd), and elasticity ratio. SRUS was used to calculate the microvascular flow rate and microvessel density (MVD) within the breast masses. Spearman correlation analysis was used to explore correlations between Emax and MVD. Receiver operating characteristic curves and nomogram were employed to assess the diagnostic efficacy of combining SRUS with SWE, using pathological results as the gold standard.

RESULTS

Emax, Emean, Esd, and MVD were significantly higher in malignant breast masses compared to benign ones ( < 0.001), while Emin was significantly lower in malignant masses ( < 0.05). In Spearman correlation analysis, Emax was significantly positively correlated with MVD ( < 0.01). The area under the curve for SRUS combined with SWE (0.924) was significantly higher than that for SWE (0.883) or SRUS (0.830) alone ( < 0.001), thus indicating improved diagnostic accuracy. The decision curve analysis of the nomogram indicated that SWE combined with SRUS model had a higher net benefit in predicting breast cancer.

CONCLUSIONS

The MVD of the breast mass shows a significant positive correlation with Emax. By integrating SRUS with SWE, this study proposes a novel diagnostic approach designed to improve specificity and accuracy in breast cancer detection, surpassing the limitations of current ultrasound-based methods. This approach shows promise for early breast cancer detection, with the potential to reduce the need for unnecessary biopsies and improve patient outcomes.