Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1760231 | Ultrasound in Medicine & Biology | 2016 | 13 Pages |
Abstract
The aim of this meta-analysis was to estimate the diagnostic performance of shear wave elastography (SWE) in differentiating malignant from benign breast lesions. A literature search of PubMed, Web of Science and Scopus up to November 2014 was conducted. A summary receiver operating characteristic curve was constructed, and pooled weighted estimates of sensitivity and specificity were calculated using a bivariate mixed-effects regression model. Thirty-three studies, which included a total of 5838 lesions (2093 malignant, 3745 benign) from 5397 patients, were finally analyzed. Summary sensitivity and specificity were 0.886 (95% confidence interval [CI], 0.858-0.909) and 0.866 (95% CI, 0.833-0.894), respectively. The pooled diagnostic odds ratio was 50.410 (95% CI, 34.972-72.664). And the area under the receiver operating characteristic curve of SWE was 0.94 (95% CI, 0.91-0.96). No publication bias existed among these studies (p = 0.245). In the subgroup analysis, sensitivity and specificity were 0.862 (95% CI, 0.811-0.901) and 0.875 (95% CI, 0.793-0.928) among 1552 lesions from 1429 patients in the 12 studies using acoustic radiation force impulse imaging and 0.897 (95% CI, 0.863-0.923) and 0.863 (95% CI, 0.831-0.889) among another 4436 lesions from 4097 patients in the 21 studies using supersonic shear imaging. When analysis confined to 9 studies evaluated the diagnostic performance of combination SWE and conventional ultrasound, the area under the curve was 0.96 (95% CI, 0.94-0.97), yielding a sensitivity of 0.971 (95% CI, 0.941-0.986) and specificity of 0.801 (95% CI, 0.733-0.856). SWE seems to be a good quantitative method for differentiating breast lesions, with promise for integration into routine imaging protocols.
Keywords
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Acoustics and Ultrasonics
Authors
Baoxian Liu, Yanling Zheng, Guangliang Huang, Manxia Lin, Quanyuan Shan, Ying Lu, Wenshuo Tian, Xiaoyan Xie,