Shear-Wave Elastography of the Breast: Impact of Technical Image Quality Parameters on Diagnostic Accuracy

OBJECTIVE. The purpose of this study is to determine the impact of shear-wave elastography (SWE) image quality parameters on the diagnostic performance of elasticity measurements in classifying breast lesions. MATERIALS AND METHODS. This retrospective study included 281 breast lesions that underwent SWE and ultrasound-guided biopsy performed between October 1, 2017, and August 31, 2018. Three readers who were blinded to pathologic outcomes independently scored the image quality of each SWE image (with low quality denoted by a score of 0 and high quality indicated by a score of 1) on the basis of five parameters: B-mode visualization of the lesion on a dual-panel display, SWE red pattern (denoting high stiffness) in the near field of the FOV, appearance of the surrounding tissue, FOV placement, and ROI placement for the maximum (E_max), minimum (E_min), mean (E_mean), and SD (E_SD) of Young modulus elasticity measurements. Using ROC analysis, we compared the performance of E_max, E_mean, and E_SD in diagnosing malignancy on low- and high-quality images on the basis of consensus (i.e., majority) scores for each individual quality parameter as well as two models combining a few of the quality parameters. RESULTS. Three quality parameters (B-mode visualization of the lesion, presence of a near-field red pattern, and the appearance of the surrounding tissue) showed moderate-to-substantial interobserver agreement. SWE images were considered high quality (n = 167) if both B-mode visualization and near-field red pattern received a consensus score of 1, and they were considered low quality (n = 114) if either parameter received a consensus score of 0. High-quality images had a statistically higher AUC value than low-quality images when E_max (p < .001), E_mean (p = .002), and E_SD (p < .001) were used as classifiers of malignancy. CONCLUSION. Quality parameters can support radiologists who are performing and interpreting breast SWE images. These quality parameters have the potential to improve the accuracy of SWE in differentiating malignant from benign breast lesions.