Zhang Juan, Dong Wei, Liu Wanmin, Fu Jiazhao, Liao Tian, Li Yinqiao, Huo Lei, Jia Ningyang
Department of Radiology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Department of Pathology, Eastern Hepatobiliary Surgery Hospital, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
Abdom Radiol (NY). 2024 Mar;49(3):710-721. doi: 10.1007/s00261-023-04130-6. Epub 2023 Dec 19.
Microvascular invasion (MVI) is a significant prognostic factor in combined hepatocellular cholangiocarcinoma (cHCC-CCA). However, its diagnosis relies on postoperative histopathologic analysis. This study aims to identify preoperative inflammatory biomarkers and MR-imaging features that can predict MVI in cHCC-CCA.
This retrospective study enrolled 119 patients with histopathologically confirmed cHCC-CCA between January 2016 and December 2021. Two radiologists, unaware of the clinical data, independently reviewed all MR image features. Univariable and multivariable analyses were performed to determine the independent predictors for MVI among inflammatory biomarkers and MRI characteristics. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic performance.
Multivariable logistic regression analysis identified four variables significantly associated with MVI (p < 0.05), including two inflammatory biomarkers [albumin-to-alkaline phosphatase ratio (AAPR) and aspartate aminotransferase-to-neutrophil ratio index (ANRI)] and two MRI features (non-smooth tumor margin and arterial phase peritumoral enhancement). A combined model for predicting MVI was constructed based on these four variables, with an AUC of 0.802 (95% CI 0.719-0.870). The diagnostic efficiency of the combined model was higher than that of the imaging model.
Inflammatory biomarkers and MRI features could be potential predictors for MVI in cHCC-CCA. The combined model, derived from inflammatory biomarkers and MRI features, showed good performance in preoperatively predicting MVI in cHCC-CCA patients.
微血管侵犯(MVI)是肝内胆管癌合并肝细胞癌(cHCC-CCA)的一个重要预后因素。然而,其诊断依赖于术后组织病理学分析。本研究旨在识别可预测cHCC-CCA中MVI的术前炎症生物标志物和磁共振成像(MR)特征。
本回顾性研究纳入了2016年1月至2021年12月期间119例经组织病理学确诊的cHCC-CCA患者。两名不了解临床数据的放射科医生独立审查了所有MR图像特征。进行单变量和多变量分析以确定炎症生物标志物和MRI特征中MVI的独立预测因素。采用受试者操作特征(ROC)曲线下面积(AUC)评估诊断性能。
多变量逻辑回归分析确定了与MVI显著相关的四个变量(p < 0.05),包括两个炎症生物标志物[白蛋白与碱性磷酸酶比值(AAPR)和天冬氨酸转氨酶与中性粒细胞比值指数(ANRI)]以及两个MRI特征(肿瘤边缘不光滑和动脉期瘤周强化)。基于这四个变量构建了一个预测MVI的联合模型,AUC为0.802(95%CI 0.719 - 0.870)。联合模型的诊断效率高于成像模型。
炎症生物标志物和MRI特征可能是cHCC-CCA中MVI的潜在预测因素。源自炎症生物标志物和MRI特征的联合模型在术前预测cHCC-CCA患者的MVI方面表现良好。