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Eur Radiol. 2010 May;20(5):1160-7. doi: 10.1007/s00330-009-1644-7. Epub 2009 Nov 5.

Automated computer-aided stenosis detection at coronary CT angiography: initial experience.

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  • 1Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Drive, MSC 226, Charleston, SC 29401, USA.

Abstract

OBJECTIVE:

To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).

METHODS:

We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.

RESULTS:

QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.

CONCLUSION:

Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study.

PMID:
19890640
[PubMed - indexed for MEDLINE]
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