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Hepatic neuroendocrine tumors: gadoxetic acid-enhanced magnetic resonance imaging findings with an emphasis on differentiation between primary and secondary tumors

机译:肝脏神经内分泌肿瘤:增亚酸增强的磁共振成像发现,重点是初级和次级肿瘤之间的分化

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摘要

Purpose: To describe the findings of magnetic resonance (MR) imaging of hepatic neuroendocrine tumors (hNET) and to identify the features that differentiate secondary from primary tumors. Methods: This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. Between August 2008 and December 2014, 50 patients with pathologically proven hNETs who underwent gadoxetic acid-enhanced MR imaging with diffusion-weighted images were included. The patients were divided into two groups according to whether they had primary (n = 17) or secondary (n = 33) hNETs. Qualitative values based on a consensus between two observers [morphologic findings, signal intensity, and enhancement pattern (poor or indeterminate; hepatocellular carcinoma-like or cholan-giocarcinoma-like)], and quantitative values (apparent diffusion coefficient) were evaluated as predictors of secondary hNETs using multivariable logistic regression and receiver operating characteristic (ROC) analysis. Results: In multivariate analysis, the presence of multiple lesions (p = 0.011), a tumor size less than 6.3 cm (p = 0.001), and a hepatocellular carcinoma-like enhancement pattern (p = 0.031) were significant independent factors for differentiating secondary from primary hNETs, and achieved a sensitivity of 91%, a specificity of 82%, and an accuracy of 88%, with a value of the area under the ROC curve of 0.931. Conclusion: Using these specific MR imaging criteria, secondary hNETs could be differentiated from primary hNETs with a high degree of accuracy in patients with histopathologically proven hNETs.
机译:目的:描述肝神经内分泌肿瘤(HNET)的磁共振(MR)成像的结果,并鉴定分化来自原发性肿瘤的二次的特征。方法:该回顾性研究由机构审查委员会批准,免除知情同意书的要求。在2008年8月和2014年12月期间,包括50例病理证明HNET的患者,接受了与扩散加权图像的乙酰基酸增强的MR成像。根据它们是否具有伯(n = 17)或仲(n = 33)HNET,患者分为两组。基于两种观察者之间的共识的定性值[形态学发现,信号强度和增强模式(差或不确定;肝细胞癌样或胆瓜 - Giocarcinoma样)]和定量值(表观扩散系数)被评价为预测因子使用多变量逻辑回归和接收器操作特征(ROC)分析的辅助HNETs。结果:在多变量分析中,多个病变的存在(P = 0.011),肿瘤大小小于6.3cm(p = 0.001),以及肝细胞癌样增强模式(p = 0.031)是区分中的显着独立因素从初级HNET,并达到91%的敏感性,特异性为82%,精度为88%,具有0.931的ROC曲线下的区域值。结论:使用这些特定的MR成像标准,次级HNET可以从初级HNET分化,具有高度精度,在具有组织病理学证明的HNET的患者中。

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