Gut Liver.  2022 Mar;16(2):277-289. 10.5009/gnl210130.

Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

  • 1Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea.
  • 2Department of Liver Transplantation and Hepatobiliary Surgery, Ajou University School of Medicine, Suwon, Korea.
  • 3Department of Radiology, Ajou University School of Medicine, Suwon, Korea.
  • 4Insight Mining Corporation, Daejeon, Korea.
  • 5Department of Radiology, Seoul St. Mary's Hospital, Seoul, Korea.


Posthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model.
A total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model.
PHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>–0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF.
Our novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.


Carcinoma, hepatocellular; Hepatectomy; Magnetic resonance elastography; Hepatic fibrosis; Liver failure
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