Predictive Value of Liver Stiffness Measurement by Magnetic Resonance Elastography for Complications after Liver...

Liver fibrosis has been recognized as a significant risk factor for short-term outcomes after hepatectomy. Magnetic resonance elastography (MRE) showed higher diagnostic performance in staging liver fibrosis than other elastography modalities. This study aimed to assess the accuracy of predicting postoperative complications in patients with malignant liver tumors using liver stiffness measurement (LSM) by MRE.

After a systematic review of the relevant studies, the sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve (AUC) for the diagnosis of postoperative complications were pooled using bivariate meta-analysis. Meanwhile, the pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the effect. Heterogeneity was explored by sensitivity analysis, univariate meta-regression, and subgroup analysis. The potential publication bias was evaluated by the Deek's funnel plot test.

Eight studies comprising a total of 1,154 patients that predicted postoperative outcomes as their purpose were ultimately included in the quantitative analysis. The pooled results of the meta-analysis revealed that the pooled sensitivity, specificity, and AUC were 78% (95% CI: 69-85%, Higgins's inconsistency index [I2] = 43.00), 75% (95% CI: 70-80%, Higgins's inconsistency index [I2] = 72.53), and 0.83 (95% CI: 0.80-0.86), respectively. Preoperative LSM by MRE was significantly associated with the development of overall postoperative outcomes (OR 1.78, 95% CI: 1.49-2.08). Univariate meta-regression showed that advanced fibrosis stage (≥F3), HCC patient proportion and cut-off value significantly influenced the heterogeneity of the included studies. The AUC of several novel prediction models based on LSM by MRE ranged from 0.818 to 0.911.

In conclusion, liver stiffness measured by MRE was a significant predictor of postoperative outcomes in patients undergoing liver resection. Future studies could focus on setting a prognostic model integrated with LSM by MRE in distinguishing patients at high risk of posthepatectomy complications.