A model based on shear wave elastography to predict post-hepatectomy liver failure in patients with hepatocellular carcinoma
10.3760/cma.j.cn131148-20191113-00699
- VernacularTitle:基于剪切波弹性成像技术构建肝细胞癌切除术后肝衰竭预测模型
- Author:
Haiyi LONG
1
;
Xiaoyan XIE
;
Liya SU
;
Xian ZHONG
;
Xiaoer ZHANG
;
Xiaohua XIE
;
Manxia LIN
Author Information
1. 中山大学附属第一医院超声医学科 中山大学超声诊断与介入超声研究所，广州 510080
- From:
Chinese Journal of Ultrasonography
2020;29(5):399-404
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a model based on two-dimensional shear wave elastography (2D-SWE) for predicting post-hepatectomy liver failure (PHLF) among patients with hepatocellular carcinoma (HCC).Methods:One hundred and one consecutive patients with HCC undergoing hepatectomy from August 2018 to July 2019 were enrolled prospectively in the First Affiliated Hospital of Sun Yat-Sen University. Laboratory tests, shear wave elastography in liver parenchyma, and abdominal contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) were performed preoperatively. Liver functional reserve, liver stiffness (LS), and tumor-related imaging parameters were assessed. PHLF was defined according to the definition of International Study Group of Liver Surgery Recommendations (ISGLS). A predictive model was developed by logistic regression analysis and the performance thereof was evaluated by receiver operating characteristic (ROC) curve analysis and Hosmer-Lemeshow test.Results:PHLF occurred in 39 patients (38.9%). Logistic regression analysis identified that international normalized ratio ( OR=1.09, P=0.026), LS( OR=1.297, P=0.004) and the largest nodule diameter( OR=1.191, P=0.015) were independent risk factors of PHLF.The area under curve (AUC) of the model was 0.842(95% CI =0.763-0.921), which was significantly higher than those of ALBI score, MELD score and Child-Pugh score (AUC 0.626-0.688, P<0.05). The model also showed good calibration in Hosmer-Lemeshow test ( P=0.498). Conclusions:A model based on 2D-SWE provides good preoperative prediction of PHLF among patients with HCC, which might have the potential in better customizing treatment strategy in those patients.
