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The diagnostic performance of MRI in osteoarthritis: a systematic review and meta-analysis
Objective:
Osteoarthritis (OA) is currently diagnosed using clinical and radiographic findings. In recent years magnetic resonance imaging (MRI) use in OA has increasingly been studied. This study was conducted to determine the diagnostic utility of MRI in OA through a meta-analysis of published studies.
Methods:
A systematic literature search was undertaken to include studies that used MRI to evaluate or detect OA. MRI was compared to various reference standards: histology, arthroscopy, radiography, CT, clinical evaluation, and direct visual inspection. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic (ROC) area under the curve (AUC) were calculated. Random-effects models were used to pool results.
Results:
Of 20 relevant studies identified from the literature, 16 reported complete data and were included in the meta-analysis, with a total of 1220 patients (1071 with OA and 149 without). Overall sensitivity from pooling data of all the included studies was 61% [95% confidence interval (CI) 53-68], specificity was 82% (95% CI 77-87), PPV was 85% (95% CI 80-88), and NPV was 57% (95% CI 43-70). The ROC showed an AUC of 0.804. There was significant heterogeneity in the above parameters (I(2)>83%). With histology as the reference standard, sensitivity increased to 74% and specificity decreased to 76% compared with all reference standards combined. When arthroscopy was used as the reference standard, sensitivity increased to 69% and specificity to 93% compared with all reference standards combined.
Conclusion:
MRI can detect OA with an overall high specificity and moderate sensitivity when compared with various reference standards, thus lending more utility to ruling out OA than ruling it in. The sensitivity of MRI is below the current clinical diagnostic standards. At this time standard clinical algorithm for OA diagnosis, aided by radiographs appears to be the most effective method for diagnosing OA.
