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Clin Imaging. 2013 Mar-Apr;37(2):256-64. doi: 10.1016/j.clinimag.2012.05.004. Epub 2012 Aug 24.

Optimization of the scanning technique and diagnosis of pulmonary nodules with first-pass 64-detector-row perfusion VCT.

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  • 1Department of CT diagnosis, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China. shengjiejiayou@126.com

Abstract

OBJECTIVE:

The aims of this study were to optimize the scanning technique of first-pass 64-detector-row perfusion volume computed tomography imaging, to evaluate the effectiveness and stability of this scan protocol, and lastly to evaluate the differential diagnosis ability of perfusion imaging in solitary pulmonary nodules (SPNs).

METHODS:

A total of 144 patients with SPNs underwent perfusion scan with 64-slice spiral CT scanner. The CT perfusion imaging was analyzed for time-density curve, perfusion parametric maps, and the respective perfusion parameters. We then analyzed the main factors concerning the imaging quality and evaluated the effectiveness of scan protocol by determining the receiver operating characteristic (ROC) curve, diagnostic efficacy, and odds ratio as well as the stability of scan protocol by consistency analysis. Immunohistochemical findings of microvessel density measurement and vascular endothelial growth factor expression were evaluated.

RESULTS:

The total sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio, and the area under ROC curve during 5-45-s scan period were 78.95%, 82.4%, 80.6%, 83.3%, 77.8%, 4.620, 0.280, and 0.840, respectively, and Kappa value was 0.894. The diagnostic efficacy of CT pulmonary perfusion was significantly higher than during 0-40-s scan period. The parameter values in different nodules were different.

CONCLUSION:

The optimized 5-45-s scan period of CT pulmonary perfusion imaging is effective in pathologic diagnosis and has good stability, worthy of being popularized. Lung perfusion CT could be a promising and feasible method for differentiation of SPNs.

Copyright © 2013. Published by Elsevier Inc.

PMID:
23465977
[PubMed - indexed for MEDLINE]
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