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中国癌症防治杂志

中国癌症防治杂志 ›› 2021, Vol. 13 ›› Issue (5): 477-483.doi: 10.3969/j.issn.1674-5671.2021.05.06

• 肝癌专栏 • 上一篇    下一篇

基于Gd-EOB-DTPA增强MRI的列线图模型在预测肝肿瘤术后肝衰竭中的临床价值 

  

  1. 陆军军医大学第一附属医院肝胆外科；Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Sweden；陆军军医大学第一附属医院放射科
  • 出版日期:2021-10-25 发布日期:2021-11-16
  • 通讯作者: 马宽生 E-mail：kuanshengma@outlook.com
  • 基金资助:
    国家自然科学基金项目（82073346）；国家留学基金项目（201907930009）；重庆英才·名家名师专项（4246ZP112）

Clinical value of a nomogram based on preoperative Gd-EOB-DTPA-enhanced MRI in predicting post-hepatectomy liver failure after liver tumor resection

  • Online:2021-10-25 Published:2021-11-16

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摘要： 目的 构建基于Gd-EOB-DTPA增强MRI的列线图模型，探讨其预测肝肿瘤术后肝衰竭（post-hapatectomy liver failure，PHLF）的临床价值。方法 回顾性分析2019年9月—2020年11月于陆军军医大学第一附属医院肝胆外科行肝肿瘤切除的117例肝癌患者的临床资料，并利用术前Gd-EOB-DTPA增强MRI肝胆期影像计算肝-肌比率（liver-to-muscle ratio，LMR），定量评估肝功能。通过单因素及多因素logistic回归分析筛选独立预测因子并构建预测模型。绘制ROC曲线和校正曲线评估模型。结果 单因素及多因素logistic回归分析筛选结果显示，终末期肝病模型（model for end-stage liver disease，MELD）评分、手术方式和LMR是预测PHLF的独立因素（均P<0.05）。基于这些因素构建的列线图预测模型的ROC曲线下面积为0.83，敏感度为91.4%，特异度为64.6%。校正曲线显示模型的一致性较好。结论 基于Gd-EOB-DTPA增强MRI和临床指标构建的列线图模型在预测PHLF中显示了较高的预测准确性，具有潜在的临床应用价值。

关键词: 原发性肝癌, Gd-EOB-DTPA增强MRI, 术后肝衰竭, 列线图, 肝功能, 预测模型

Abstract: Objective To construct a nomogram model based on Gd-EOB-DTPA enhanced MRI and to investigate its clinical value in predicting post-hepatectomy liver failure (PHLF). Methods The clinical data of 117 hepatocellular carcinoma patients who underwent liver resection at the Department of Hepatobiliary Surgery, First Affiliated Hospital of Army Medical University from September 2019 to November 2020 were retrospectively analyzed. Liver-to-muscle ratio (LMR) was calculated by hepatobiliary phase images of preoperative Gd-EOB-DTPA enhanced MRI to quantitatively evaluate liver function. The independent predictors were screened by univariable and multivariable logistic regression analysis and the prediction model was constructed. Meanwhile, the receiver operating characteristic (ROC) curve and calibration curve were drawn. Results The univariable and multivariable logistic regression analysis showed that MELD score, surgical approach and LMR were independent factors in predicting PHLF (P<0.05). The area under the ROC curve of the model was 0.83, with sensitivity of 91.4% and specificity of 64.6%. The calibration curve showed a good consistency of the model. Conclusions The nomogram model constructed based on Gd-EOB-DTPA enhanced MRI and clinical predictors shows high accuracy in predicting liver failure after liver tumor surgery, and has potential clinical application value.

Key words: Primary liver cancer, Gd-EOB-DTPA enhanced MRI, Post-hepatectomy liver failure, Nomogram, Liver function, Prediction model

中图分类号: 

  • R735.7