Department of Radiology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Republic of Korea.
Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Abdom Radiol (NY). 2018 Mar;43(3):647-654. doi: 10.1007/s00261-017-1244-y.
To assess whether morphologic analysis using computed tomography (CT) could differentiate between fat-poor angiomyolipoma (fpAML) and renal cell carcinoma (RCC).
A total of 602 patients with a histologically confirmed fpAML (n = 49) or RCC (n = 553) were evaluated. All renal lesions were less than 4 cm in size and had no gross fat on contrast-enhanced CT. For morphologic analysis, overflowing beer sign and angular interface were evaluated. Overflowing beer sign was defined as contact length between bulging-out portion of a mass and the adjacent renal capsule of 3 mm or greater. Angular interface was defined as the angle of parenchymal portion of a mass of 90° or less. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were assessed. Multivariate analysis was conducted to determine which variable is predictive of fpAML.
Sensitivity, specificity, PPV, NPV, and accuracy were 61.2% (30/49), 97.1% (537/553), 65.2% (30/46), 96.6% (537/556), and 94.2% (567/602) with overflowing beer sign, while they were 55.1% (27/49), 81.9% (453/553), 21.3% (27/127), 95.4% (453/475), and 79.7% (480/602) with angular interface for fpAML, respectively. Both CT variables were predictive of fpAML (overflowing beer sign, odds ratio = 132.881, p < 0.001; angular interface, odds ratio = 5.766, p = 0.010). The multivariate model with CT variables showed good performance for predicting fpAML (AUC, 0.871 with angular interface, 0.943 with overflowing beer sign, and 0.949 with both).
Morphologic analysis with contrast-enhanced CT may be useful for differentiating fpAML from RCC. Overflowing beer sign has the potential as an imaging biomarker for fpAML.
评估使用计算机断层扫描(CT)的形态分析是否可以区分乏脂性血管平滑肌脂肪瘤(fpAML)和肾细胞癌(RCC)。
共评估了 602 名经组织学证实的 fpAML(n=49)或 RCC(n=553)患者。所有肾病变均小于 4cm 且在对比增强 CT 上无明显脂肪。进行形态分析时,评估了溢出啤酒征和角状界面。溢出啤酒征定义为肿块外凸部分与相邻肾包膜的接触长度为 3mm 或更长。角状界面定义为肿块实质部分的角度为 90°或更小。评估了敏感性、特异性、阳性预测值(PPV)、阴性预测值(NPV)和准确性。进行了多变量分析以确定哪种变量可预测 fpAML。
溢出啤酒征的敏感性、特异性、PPV、NPV 和准确性分别为 61.2%(30/49)、97.1%(537/553)、65.2%(30/46)、96.6%(537/556)和 94.2%(567/602),而角状界面分别为 55.1%(27/49)、81.9%(453/553)、21.3%(27/127)、95.4%(453/475)和 79.7%(480/602)。两种 CT 变量均能预测 fpAML(溢出啤酒征,优势比=132.881,p<0.001;角状界面,优势比=5.766,p=0.010)。具有 CT 变量的多变量模型可很好地预测 fpAML(AUC:角状界面为 0.871,溢出啤酒征为 0.943,两者均为 0.949)。
增强 CT 的形态分析可能有助于区分 fpAML 和 RCC。溢出啤酒征有可能成为 fpAML 的影像学生物标志物。