Diagnostic Accuracy of Hepatic MRI-PDFF and R2 for the Evaluation of Liver Steatosis and Liver Iron Overload: A Meta-analysis - Yuan, Kecheng; Liu, Qingyun; Huangfu, Xuhe; Luo, Penghui; Wang, Changliang; Qi, Fulang; Chen, Lin; Qiu, Bensheng (Academic radiology, 2025)
To evaluate the diagnostic accuracy of hepatic magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) and R2* using the multi-echo Dixon technique for the evaluation of liver steatosis (LS) and liver iron overload (LIO). The study focuses on the determination of optimal cutoff values and the area under the summary receiver operating characteristic curve (AUROC) as primary measures of diagnostic performance. A systematic literature search was conducted by two independent researchers. The diagnostic accuracy of studies was assessed using the quality assessment of diagnostic accuracy studies-2. The AUROCs of MRI-PDFF (for LS grades: 0 versus 1-3, 0-1 versus 2-3, and 0-2 versus 3) and R2* (for LIO grades: 0 versus 1-4) were compared and analyzed. Meta-regression analyses were performed to explore sources of heterogeneity in MRI-PDFF. The review followed the preferred reporting items for systematic reviews and meta-analyses guidelines. 29 studies with 4548 subjects (2565 males) were investigated. The summary AUROC values of MRI-PDFF for classifying LS grades 0 versus 1-3, 0-1 versus 2-3, and 0-2 versus 3 were 0.95 (95% confidence interval (CI): 0.93-0.97), 0.93 (95% CI: 0.91-0.95), and 0.93 (95% CI: 0.91-0.95), respectively. The summary AUROC values of R2* for classifying LIO grades 0 versus 1-4 was 0.81 (95% CI: 0.78-0.84). The optimal MRI-PDFF cutoffs for diagnosing LS were identified as 4.4% (LS ≥ G1), 13.34% (LS ≥ G2), and 16.9% (LS ≥ G3), while the optimal R2* cutoffs for LIO ≥ G1 were 60.5 s⁻¹ (3 T MRI). MRI-PDFF and R2* demonstrate high diagnostic accuracy for evaluating LS and LIO, with established optimal cutoff values supporting their clinical utility as non-invasive diagnostic tools. These findings provide a standardized reference for MRI-based liver fat and iron quantification in clinical practice.