Accuracy of shear wave elastography for the diagnosis of prostate cancer: A meta-analysis
Many studies have established the high diagnostic accuracy of shear wave elastography (SWE) for the detection of prostate cancer (PCa); however, its utility remains a subject of debate. This meta-analysis sought to appraise the overall accuracy of SWE for the detection of PCa. A literature search of...
Saved in:
Published in | Scientific reports Vol. 7; no. 1; pp. 1949 - 8 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
16.05.2017
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Many studies have established the high diagnostic accuracy of shear wave elastography (SWE) for the detection of prostate cancer (PCa); however, its utility remains a subject of debate. This meta-analysis sought to appraise the overall accuracy of SWE for the detection of PCa. A literature search of the PubMed, Embase, Cochrane Library, Web of Science and CNKI (China National Knowledge Infrastructure) databases was conducted. In all of the included studies, the diagnostic accuracy of SWE was compared with that of histopathology, which was used as a standard. Data were pooled, and the sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated to estimate the accuracy of SWE. The pooled sensitivity and specificity for the diagnosis of PCa by SWE were 0.844 (95% confidence interval: 0.696–0.927) and 0.860 (0.792–0.908), respectively. The AUC was 0.91 (0.89–0.94), the PLR was 6.017 (3.674–9.853), and the NLR was 0.182 (0.085–0.389). The DOR was 33.069 (10.222–106.982). Thus, SWE exhibited high accuracy for the detection of PCa using histopathology as a diagnostic standard. Moreover, SWE may reduce the number of core biopsies needed. |
---|---|
Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 2045-2322 2045-2322 |
DOI: | 10.1038/s41598-017-02187-0 |