Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, 200072, China.
Thyroid Institute, Tongji University School of Medicine, Shanghai, 200072, China.
Sci Rep. 2017 Aug 1;7(1):7036. doi: 10.1038/s41598-017-07389-0.
To evaluate the diagnostic performance of shear wave arrival time contour (SWATC) display for the diagnosis of breast lesions and to identify factors associated with the quality of shear wave propagation (QSWP) in breast lesions. This study included 277 pathologically confirmed breast lesions. Conventional B-mode ultrasound characteristics and shear wave elastography parameters were computed. Using the SWATC display, the QSWP of each lesion was assigned to a two-point scale: score 1 (low quality) and score 2 (high quality). Binary logistic regression analysis was performed to identify factors associated with QSWP. The area under the receiver operating characteristic curve (AUROC) for QSWP to differentiate benign from malignant lesions was 0.913, with a sensitivity of 91.9%, a specificity of 90.7%, a positive predictive value (PPV) of 74.0%, and a negative predictive value (NPV) of 97.5%. Compared with using the standard deviation of shear wave speed (SWS) alone, SWS combined with QSWP increased the sensitivity from 75.8% to 93.5%, but decreased the specificity from 95.8% to 89.3% (P < 0.05). SWS was identified to be the strongest factor associated with the QSWP, followed by tumor malignancy and the depth of the lesion. In conclusion, SWATC display may be useful for characterization of breast lesions.
评估剪切波到达时间轮廓(SWATC)显示在诊断乳腺病变中的诊断性能,并确定与乳腺病变中剪切波传播质量(QSWP)相关的因素。本研究纳入了 277 个经病理证实的乳腺病变。计算了常规 B 型超声特征和剪切波弹性成像参数。使用 SWATC 显示,将每个病变的 QSWP 分配到两点评分:评分 1(质量低)和评分 2(质量高)。采用二项逻辑回归分析确定与 QSWP 相关的因素。QSWP 区分良性和恶性病变的受试者工作特征曲线(AUROC)下面积为 0.913,敏感性为 91.9%,特异性为 90.7%,阳性预测值(PPV)为 74.0%,阴性预测值(NPV)为 97.5%。与单独使用剪切波速度标准差(SWS)相比,SWS 结合 QSWP 提高了敏感性(从 75.8%提高至 93.5%),但降低了特异性(从 95.8%降至 89.3%)(P<0.05)。SWS 是与 QSWP 相关性最强的因素,其次是肿瘤恶性程度和病变深度。总之,SWATC 显示可能有助于乳腺病变的特征描述。