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

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.

Full article: Eur Radiol. 2009 Nov 5. [Epub ahead of print]

Authors:
Elisabeth Arnoldi, Mulugeta Gebregziabher, U. Joseph Schoepf, Roman Goldenberg, Luis Ramos-Duran, Peter L. Zwerner, Konstantin Nikolaou, Maximilian F. Reiser, Philip Costello, Christian Thilo