Distinguishing benign from malignant parotid gland tumours: low-dose multi-phasic CT protocol with 5-minute delayReport as inadecuate




Distinguishing benign from malignant parotid gland tumours: low-dose multi-phasic CT protocol with 5-minute delay - Download this document for free, or read online. Document in PDF available to download.

European Radiology

, Volume 21, Issue 8, pp 1692–1698

First Online: 11 March 2011Received: 12 November 2010Revised: 12 February 2011Accepted: 18 February 2011

Abstract

ObjectivesTo explore the percentage enhancement wash-out ratio PEW and relative PEW RPEW of low-dose multi-phasic computed tomography CT in distinguishing benign from malignant parotid gland tumours.

MethodsThis study was approved by the ethics committee, and informed patient consent was obtained. 51 patients with parotid tumours proven by histopathology received CT, including 18 with pleomorphic adenomas, 14 with Warthin’s tumours and 19 with malignant tumours. Size and attenuation of parotid tumours were measured. Compared with 5-min attenuation, the 30-s and 90-s PEW PEW30, PEW90 and RPEW RPEW30, RPEW90 were calculated.

ResultsThere was a significant difference in PEW30, RPEW30, PEW90 and RPEW90 in the parotid neoplasms groups P < 0.01, and statistical significance existed simultaneously in pleomorphic adenomas vs malignant tumours and Warthin’s tumours vs malignant tumours according to SNK-q test. The optimal diagnosis results of malignancy with 100% specificity 32-32 was obtained by using a combination of the following criteria: −70% > PEW30 < 36%, −30% > PEW30 < 19%, PEW90 > 12%, and the sensitivity 74% for diagnosis of malignancy was yield.

ConclusionsWash-out ratio may assist in differentiating the benign from malignant parotid gland tumours. Combining the percentage of enhanced wash-out ratios of CT protocols can yield diagnostic results for malignancy.

KeywordsLow-dose CT·parotid gland neoplasm Dynamic contrast-enhanced CT Percentage enhancement wash-out ratio  Download fulltext PDF



Author: G. Q. Jin - D. K. Su - D. Xie - W. Zhao - L. D. Liu - X. N. Zhu

Source: https://link.springer.com/







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