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多层螺旋CT动态增强不同时相廓清参数在孤立性肺结节鉴别诊断中的价值 被引量:4

Washout characteristics at contrast-enhanced MSCT for diagnosis of solitary pulmonary nodule:A comparison with ROC analysis
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摘要 目的比较多层螺旋CT(MDCT)动态增强不同时相廓清参数在孤立性肺结节(SPN)鉴别诊断中的价值。方法用MDCT扫描机对SPN行平扫和增强后20 s、30 s、45 s、60 s、75 s、90 s、2 min、3 min、5 min、9 min、12 min、15 min、20 min共13个序列扫描,计算结节5 min、9 min、12 min、15 min、20 min等不同时相廓清参数,接受者操作特性曲线(ROC)分析比较廓清参数在肺结节诊断中的效能,用曲线下面积(AUC)表示。结果共59例患者(男性42例、女性17例;年龄32~80岁)59个结节[平均最长径(1.8±0.8)cm]纳入本研究。所有各时相廓清参数的AUC为0.636~0.780,最高效能出现在增强后9 min时相的廓清率指标,其AUC为0.780。各时相廓清参数诊断效能差异不显著。结论 CT动态增强廓清参数是诊断SPN的有效方法,合理缩短延迟时间能明显提高工作效率,而廓清参数的诊断效能无明显下降。 Objective To evaluate the value of washout parameters at contrast-enhanced multi-detector computed tomography(MDCT) in the diagnosis of solitary pulmonary nodules(SPNs). Methods SPNs were evaluated by dynamicenhanced MDCT. The images were obtained before and at 20 s, 30 s, 45 s, 60 s, 75 s, 90 s, 2 min, 3 min, 5 min, 9 min, 12 min, 15 min and 20 min after the injection of contrast media. The washout parameters of each lesion were calculated. The value of washout parameters in the diagnosis of SPNs were expressed as area under curve(AUC) by receiver operating characteristic curve(ROC) analysis. Results Fifty-nine patients(42 male, 17 female; age ranging 32-80 years) with 59 SPNs [mean diameter(1.8±0.8) cm] were included in this study. AUC of washout parameters ranged from 0.636 to 0.780. The maximum AUC value of 0.780 appeared at 9-minute. There were no significant differences in the value of washout parameters in diagnosis of SPNs. Conclusion The washout parameters on delayed enhanced CT scans can improve the accuracy of diagnosis of SPNs. The reasonable delayed enhanced CT scanning could be recommended in clinical application.
出处 《肿瘤影像学》 2013年第4期296-299,共4页 Oncoradiology
基金 国家自然科学基金(No:81301218 81301262) 上海市科委项目(No:124119a0100) 上海交通大学附属胸科医院项目(No:YZ 12-19) 1050人才培养计划
关键词 肺肿瘤 孤立性肺结节 计算机断层扫描 X线 廓清 接受者操作特性曲线 Pulmonary neoplasm Solitary pulmonary nodule Computed tomography X-ray Washout Receiver-operating characteristic curve
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