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Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
Authors:Hyo Jung Cho  Young Hwan Ahn  Min Suh Sim  Jung Woo Eun  Soon Sun Kim  Bong Wan Kim  Jimi Huh  Jei Hee Lee  Jai Keun Kim  Buil Lee  Jae Youn Cheong  Bohyun Kim
Affiliation:1.Departments of Gastroenterology, Ajou University School of Medicine, Suwon, Korea;2.Departments of Liver Transplantation and Hepatobiliary Surgery, Ajou University School of Medicine, Suwon, Korea;3.Departments of Radiology, Ajou University School of Medicine, Suwon, Korea;4.Insight Mining Corporation, Daejeon, Korea;5.Department of Radiology, Seoul St. Mary''s Hospital, Seoul, Korea
Abstract:
Background/AimsPosthepatectomy 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.MethodsA 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.ResultsPHLF 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.ConclusionsOur novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.
Keywords:Carcinoma, hepatocellular   Hepatectomy, Magnetic resonance elastography   Hepatic fibrosis   Liver failure
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