Comparative Diagnostic Accuracy of Magnetic Resonance Elastography and Diffusion-Weighted Imaging in Differentiating Benign and Malignant Focal Liver Lesions: A Systematic Review and Meta-Analysis
Hassankhani, A.; Valizadeh, P.; Jannatdoust, P.; Amoukhteh, M.; Mohammadi, A.; Gholamrezanezhad, A.; Haq, A.
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BackgroundAccurate differentiation of benign and malignant focal liver lesions (FLLs) is essential for clinical decision-making. Magnetic resonance elastography (MRE) and diffusion-weighted imaging (DWI) are advanced MRI techniques used for noninvasive lesion characterization, but their comparative diagnostic performance has not been definitively established. ObjectiveTo systematically compare the diagnostic accuracy of MRE and DWI for distinguishing benign from malignant FLLs. MethodsA systematic review and meta-analysis were conducted following PRISMA guidelines. PubMed, Embase, and Scopus were searched through July 2025 for studies directly comparing MRE and DWI in the same patient cohorts with focal liver lesions, using histopathology or validated imaging follow-up as the reference standard. Sensitivity, specificity, and area under the curve (AUC) were pooled using bivariate random-effects models, with paired analysis to compare modalities. Results219 patients with 284 focal liver lesions were analyzed. MRE demonstrated higher pooled sensitivity (93.8%, 95% CI: 85.6-97.5) and specificity (89.9%, 95% CI: 74.6-96.4) than DWI (sensitivity 86.2%, 95% CI: 80.5-90.5; specificity 83.4%, 95% CI: 74.3-89.8). MRE also had a higher AUC (0.97 vs. 0.88). Likelihood ratio analysis indicated MREs stronger ability to both confirm and exclude malignancy. Paired meta-analysis confirmed a statistically significant increase in sensitivity for MRE (relative sensitivity 1.09; p = 0.018), with no significant difference in specificity. ConclusionMRE demonstrates superior sensitivity and overall diagnostic accuracy compared to DWI for differentiating benign and malignant FLLs. Further large-scale prospective studies are needed to confirm these results and determine optimal cutoff values to guide clinical decision-making.
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