Assessment of Hepatic Vascular Network Connectivity with Automated Graph Analysis of Dynamic Contrast-enhanced US to Evaluate Portal Hypertension in Patients with Cirrhosis: A Pilot Study
Assessment of Hepatic Vascular Network Connectivity with Automated Graph Analysis of Dynamic Contrast-enhanced US to Evaluate Portal Hypertension in Patients with Cirrhosis: A Pilot Study
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DOI：
10.1148/radiol.2015141941
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发表时间：
2015-10-01
期刊：
影响因子：
19.7
通讯作者：
Bosch, Jaime
中科院分区：
文献类型：
--
作者：
Amat-Roldan, Ivan;Berzigotti, Annalisa;Bosch, Jaime
目的：测试肝脏动态对比材料增强（DCE）超声（US）血管图像的图形分析是否可以计算肝脏循环的组织程度，以及图形性质是否与门静脉高压症的严重程度相关。材料和方法：获得机构审查委员会批准并获得书面知情同意。本研究纳入2007年1月至2008年12月间接受DCE - US和肝静脉压梯度（HVPG）测量的15例肝硬化患者（9例男性，平均年龄6标准差，55岁6 8）和4例健康受试者（2男2女，平均年龄34岁6 4）。根据DCE US检查视频序列的时间序列分析（采用中断-再灌注技术）计算单个图形模型（“血管连接体”）。进行图分析，计算聚类系数C。分析聚类系数与HVPG的相关性。结果：健康受试者的血管连接组聚类系数高（C = 0.4447；四分位间距[IQR]， 0.3864-0.4679），表明肝脏血管网络高度组织。相反，肝硬化患者的聚类系数较低，表明正常解剖结构被破坏（C = 0.0288； IQR为0.0157-0.0861;P = 0.001）。聚类系数随HVPG升高而降低，HVPG≥10 mm Hg患者的聚类系数为0.0237 (IQR, 0.0066-0.0378)，而HVPG≤10 mm Hg患者的聚类系数为0.1180 (IQR, 0.0987-0.1414) （P = 0.006）。由血管连接组聚类系数（10个箱）分布得出的最佳模型与HVPG相关性为0.977（均方根误差1.57 mm Hg, P < 0.0001）。结论：本初步研究表明，基于肝脏DCE US检查视频处理的血管连通性图形建模和随后的图形分析，可以计算出反映肝脏微血管网络组织程度的个性化参数，并与肝硬化门脉高压的严重程度相关。(c) rsna, 2015
Purpose: To test whether graph analysis of vascular images obtained with hepatic dynamic contrast material-enhanced (DCE) ultrasonography (US) allows calculation of the degree of organization of the liver circulation and whether graph properties are correlated to the severity of portal hypertension.Materials and Methods: Institutional review board approval and written informed consent were obtained. Fifteen patients with liver cirrhosis (nine men; mean age 6 standard deviation, 55 years 6 8) who underwent DCE US and hepatic venous pressure gradient (HVPG) measurement and four healthy subjects (two men and two women; mean age, 34 years 6 4) were included between January 2007 and December 2008. Individual graph models ("vascular connectomes") were computed on the basis of time series analysis of video sequences of DCE US examinations (conducted with the disruption-reperfusion technique). Graph analysis was performed, and the clustering coefficient C was calculated. Correlations between clustering coefficient and HVPG were assessed.Results: Healthy subjects had a high clustering coefficient of vascular connectome (C = 0.4447; interquartile range [IQR], 0.3864-0.4679), suggesting a highly organized hepatic vascular network. Conversely, patients with cirrhosis showed a low clustering coefficient, indicating disruption of normal anatomy (C = 0.0288; IQR, 0.0157-0.0861; P = .001 vs healthy subjects). The clustering coefficient decreased as HVPG increased, with a clustering coefficient of 0.0237 (IQR, 0.0066-0.0378) in patients with HVPG of at least 10 mm Hg versus 0.1180 (IQR, 0.0987-0.1414) in those with HVPG of less than 10 mm Hg (P = .006). The correlation between the best model derived from the distribution of the clustering coefficient (10 bins) of vascular connectome and HVPG had a Pearson correlation of 0.977 (root mean squared error, 1.57 mm Hg; P < .0001).Conclusion: This pilot study demonstrates that graph modeling of vascular connectivity based on video processing of liver DCE US examinations and subsequent graph analysis enable calculation of personalized parameters that reflect the degree of organization of the hepatic microvascular network and are correlated to the severity of portal hypertension in cirrhosis. (C) RSNA, 2015