Repeatability of MR Elastography of Liver: A Meta-Analysis.

Purpose To perform a meta-analysis to generate an estimate of the repeatability coefficient (RC) for magnetic resonance (MR) elastography of the liver. Materials and Methods A systematic search of databases was performed for publications on MR elastography during the 10-year period between 2006 and 2015. The identified studies were screened independently and were verified reciprocally by all authors. Two reviewers independently determined the percentage RC and effective sample size from each article. A forest plot was constructed of the percentage RC estimates from the 12 studies. Bootstrap 95% confidence intervals (CIs) were constructed for the summary percentage RCs. Results Twelve studies comprising 274 patients met the eligibility criteria and were included for analysis. A flow diagram of studies included according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was prepared for the inclusion and exclusion criteria. All studies included in the meta-analysis fulfilled four or more of the seven categories of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2. The estimated summary RC was 22% (95% CI: 16.1%, 28.2%). The three main sources for this heterogeneity were the trained versus untrained operator drawing contours to choose regions of interest, the time between two replicate examinations, and, finally, the field strength of the MR imaging unit. The RC estimates tended to be higher for studies that did not use a well-trained operator, those with 1.5-T field strength imaging units, and those with longer time intervals between examinations. Conclusion The meta-analysis results provide the basis for the following draft longitudinal Quantitative Imaging Biomarkers Alliance MR elastography claim: A measured change in hepatic stiffness of 22% or greater, at the same site and with use of the same equipment and acquisition sequence, indicates that a true change in stiffness has occurred with 95% confidence. © RSNA, 2017.