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Reproducibility of Semi-Automated Measurement of Carotid Stenosis on CTA

Published online by Cambridge University Press:  02 December 2014

Jeremy H. White ,
Eric S. Bartlett ,
Aditya Bharatha ,
Richard I. Aviv ,
Allan J. Fox ,
Andrew L. Thompson ,
Richard Bitar  and
Sean P. Symons
Jeremy H. White
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Eric S. Bartlett
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Aditya Bharatha
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Richard I. Aviv
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Allan J. Fox
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Andrew L. Thompson
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Richard Bitar
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
Sean P. Symons*
Affiliation:
Division of Neuroradiology, Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
*
Division of Neuroradiology, Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, AG31D, Toronto, Ontario, M4N 3M5, Canada.
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Abstract

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Purpose:

To compare the reproducibility of semi-automated vessel analysis software to manual measurement of carotid artery stenosis on computed tomography angiography (CTA).

Methods:

Two observers separately analyzed 81 carotid artery CTAs using semi-automated vessel analysis software according to a blinded protocol. The software measured the narrowest stenosis in millimeters (mm), distal internal carotid artery (ICA) in mm, and calculated percent stenosis based on NASCET criteria. One observer performed this task twice on each carotid, the second analysis delayed two months in order to mitigate recall bias. Two other observers manually measured the narrowest stenosis in mm, distal ICA in mm, and calculated NASCET percent stenosis in a blinded fashion. Correlation coefficients were calculated for each group comparing the narrowest stenosis in mm, distal ICA in mm, and NASCET percent stenosis.

Results:

The semi-automated vessel analysis software provided excellent intraobserver correlation for narrowest stenosis in mm, distal ICA in mm, and NACSET percent stenosis (Pearson correlation coefficients of 0.985, 0.954, and 0.977 respectively). The semi-automated vessel analysis software provided excellent interobserver correlation (0.925, 0.881, and 0.892 respectively). The interobserver correlation for manual measurement was good (0.595, 0.625, and 0.555 respectively). There was a statistically significant difference in the interobserver correlation between the semi-automated vessel analysis software observers and the manual measurement observers (P < 0.001).

Conclusion:

Semi-automated vessel analysis software is a highly reproducible method of quantifying carotid artery stenosis on CTA. In this study, semi-automated vessel analysis software determination of carotid stenosis was shown to be more reproducible than manual measurement.

Type
Original Article
Information
Canadian Journal of Neurological Sciences , Volume 37 , Issue 4 , July 2010 , pp. 498 - 503
Copyright
Copyright © The Canadian Journal of Neurological 2010

References

1. Bartlett, ES, Walters, TD, Symons, SP, Fox, AJ. Quantification of carotid stenosis on CT angiography. Am J Neuroradiol. 2006;27 (1):13–9.Google Scholar
2. Anderson, GB, Ashforth, R, Steinke, DE, Ferdinandy, R, Findlay, JM. CT angiography for the detection and characterization of carotid artery bifurcation disease. Stroke. 2000;31(9):2168–74.Google Scholar
3. Randoux, B, Marro, B, Koskas, F, Duyme, M, Sahel, M, Zouaoui, A, et al. Carotid artery stenosis: prospective comparison of CT, three-dimensional gadolinium-enhanced MR, and conventional angiography. Radiology. 2001;220(1):179–85.Google Scholar
4. Leclerc, X, Godefroy, O, Pruvo, JP, Leys, D. Computed tomographic angiography for the evaluation of carotid artery stenosis. Stroke. 1995;26(9):1577–81.Google Scholar
5. North American symptomatic carotid endarterectomy trial collaborators. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. NEJM. 1991;325:445–53.Google Scholar
6. Rothwell, PM, Eliasziw, M, Gutnikov, SA, Fox, AJ, Taylor, DW, Mayberg, MR, et al. Analysis of pooled data from the randomized controlled trials of endarterectomy for symptomatic carotid stenosis. Lancet. 2003;361(9352):107–16.Google Scholar
7. Levinson, MM, Rodriguez, DI. Endarterectomy for preventing stroke in symptomatic and asymptomatic carotid stenosis: review of clinical trials and recommendations for surgical therapy. Heart Surg Forum. 1999;2(2):147–68.Google Scholar
8. Zhang, Z, Berg, M, Ikonen, A, Kononen, M, Kalviainen, R, Manninen, H, et al. Carotid stenosis degree in CT angiography: assessment based on luminal area versus luminal diameter measurements. Eur Radiol. 2005;15(11):2359–65.Google Scholar
9. Zhang, Z, Berg, MH, Ikonen, AE, Vanninen, RL, Manninen, HI. Carotid artery stenosis: reproducibility of automated 3D CT angiography analysis method. Eur Radiol. 2004;14(4):665–72.CrossRefGoogle ScholarPubMed
10. Silvennoinen, HM, Ikonen, S, Soinne, L, Railo, M, Valanne, L. CT angiographic analysis of carotid artery stenosis: comparison of manual assessment, semiautomatic vessel analysis, and digital subtraction angiography. Am J Neuroradiol. 2007;28(1):97–103.Google Scholar
11. Fox, AJ, Eliasziw, M, Rothwell, PM, Schmidt, MH, Warlow, CP, Barnett, HJ. Identification, prognosis, and management of patients with carotid artery near occlusion. Am J Neuroradiol. 2005;26(8):2086–94.Google Scholar
12. Fox, AJ. How to measure carotid stenosis. Radiology. 1993;186(2): 316–8.Google Scholar
13. Dix, JE, McNulty, BJ, Kalimes, DF. Frequency and significance of a small distal ICA in carotid stenosis. Am J Neuroradiol. 1998;19(7):1215–18.Google Scholar
14. Bartlett, ES, Walters, TD, Symons, SP, Fox, AJ. Diagnosing carotid stenosis near-occlusion by using CT angiography. Am J Neuroradiol. 2006;27(3):632–7.Google Scholar
15. Adame, IM, de Koning, PJ, Lelieveldt, BP, Wasserman, BA, Reiber, JH, van der Geest, RJ. An integrated automated analysis method for quantifying stenosis and plaque burden from carotid MRI images: combined postprocessing of MRA and vessel wall MR. Stroke. 2006;37(8):2162–4.Google Scholar
16. van Bemmel, CM, Elgersma, OE, Vonken, EJ, Fiorelli, M, van Leeuwen, MS, Niessen, WJ. Evaluation of semiautomated internal carotid artery stenosis quantification from 3-dimensional contrast-enhanced magnetic resonance angiograms. Invest Radiol. 2004;39(7):418–26.Google Scholar
17. Habibi, R, Lell, MM, Steiner, R, Ruehm, SG, Sayre, JW, Nael, K, et al. High-resolution 3T MR angiography of the carotid arteries: comparison of manual and semiautomated quantification of stenosis. Am J Neuroradiol. 2009;30(1):46–52.Google Scholar
18. Liu, Y, Hopper, KD, Mauger, DT, Addis, KA. CT angiographic measurement of the carotid artery: optimizing visualization by manipulating window and level settings and contrast material attenuation. Radiology. 2000;217(2):494–500.Google Scholar