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高肉芽肿病流行地区,用 F-18 FDG PET 进行动态检测鉴别孤立性肺结节。

Solitary pulmonary nodules differentiated by dynamic F-18 FDG PET in a region with high prevalence of granulomatous disease.

机构信息

Department of Nuclear Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan, ROC.

出版信息

J Radiat Res. 2012;53(2):306-12. doi: 10.1269/jrr.11089. Epub 2012 Feb 25.

Abstract

This study determined whether dynamic F-18 FDG PET imaging could differentiate benign from malignant solitary pulmonary nodules (SPNs). Histopathologically confirmed SPNs (10-35 mm), 24 malignant and 10 benign, from 34 patients were studied through both dynamic and static F-18 FDG PET imaging of all patients. Volumes of interest (VOIs) were placed over the pulmonary nodules using a 50% maximum pixel value threshold. The arterial input function was estimated from a left ventricle-defined VOI. Based on Patlak analysis, we calculated the net FDG phosphorylation rate (K(i)) and glucose metabolic rate (MRGlu) of each nodule. The slope values of the time-activity curves (TACs) of the nodules were also determined. Based on the static PET images, maximum and mean standardized uptake values (SUV(max) and SUV(mean), respectively) were calculated. Benign and malignant SPNs had significantly different values for SUV(max), SUV(mean), K(i), MRGlu, and TAC slope, with area under the receiver operating characteristic curves distinguishing benign from malignant nodules. McNemar's test of marginal homogeneity found all the predictors helpful to detect malignant nodules (all, p > 0.05), and combining K(i) and MRGlu, which were generated by dynamic study, yielded a higher specificity of 90%, and a sensitivity of 79%. Among the 10 benign nodules, static SUV imaging correctly classified seven, while dynamic F-18 PET imaging correctly classified nine. Dynamic F-18 FDG PET imaging is valuable in differentiating benign from malignant SPNs, particularly for granulomatous disease.

摘要

本研究旨在探讨动态 F-18 FDG PET 成像能否鉴别良恶性孤立性肺结节(SPN)。对 34 例患者的 24 个恶性和 10 个良性经组织病理学证实的 SPN(10-35mm)进行了动态和静态 F-18 FDG PET 成像。使用 50%最大像素值阈值在肺结节上放置感兴趣区(VOI)。通过左心室定义的 VOI 估算动脉输入函数。基于 Patlak 分析,计算每个结节的 FDG 磷酸化率(K(i))和葡萄糖代谢率(MRGlu)。还确定了结节时间-活性曲线(TAC)的斜率值。基于静态 PET 图像,计算最大和平均标准化摄取值(SUV(max)和 SUV(mean))。良性和恶性 SPN 的 SUV(max)、SUV(mean)、K(i)、MRGlu 和 TAC 斜率值存在显著差异,ROC 曲线下面积有助于鉴别良恶性结节。边际同质性 McNemar 检验发现所有预测因子均有助于检测恶性结节(均,p > 0.05),结合动态研究生成的 K(i)和 MRGlu 可提高特异性至 90%,敏感性至 79%。在 10 个良性结节中,静态 SUV 成像正确分类了 7 个,而动态 F-18 PET 成像正确分类了 9 个。动态 F-18 FDG PET 成像对鉴别良恶性 SPN 有价值,尤其是对肉芽肿性疾病。

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