Automated Quantification of Stenosis Severity on 64-Slice CTA Comparison With Quantitative Coronary Angiography
Mark J. Boogers, MD*, ,
Joanne D. Schuijf, PhD*,
Pieter H. Kitslaar, MSc ,
Jacob M. van Werkhoven, MSc*, ,
Fleur R. de Graaf, MD*,
Eric Boersma, PhD ,
Joëlla E. van Velzen, MD*, ,
Jouke Dijkstra, PhD ,
Isabel M. Adame, PhD||,
Lucia J. Kroft, MD, PhD¶,
Albert de Roos, MD, PhD¶,
Joop H.M. Schreur, MD, PhD#,
Mark W. Heijenbrok, MD**,
J. Wouter Jukema, MD, PhD*,
Johan H.C. Reiber, PhD ,||,
Jeroen J. Bax, MD, PhD*,*
* Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
Interuniversity Cardiology Institute of the Netherlands, Utrecht, the Netherlands
Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, the Netherlands
Department of Epidemiology and Statistics, Erasmus University, Rotterdam, the Netherlands
|| Medis Medical Imaging Systems BV, Leiden, the Netherlands
¶ Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
# Department of Cardiology, Medical Center Haaglanden, the Hague, the Netherlands
** Department of Radiology, Medical Center Haaglanden, the Hague, the Netherlands
* Reprint requests and correspondence: Dr. Jeroen J. Bax, Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, the Netherlands, Postal zone 2300 RC (Email: j.j.bax{at}lumc.nl).
Objectives: This study sought to demonstrate the feasibility of a dedicated algorithm for automated quantification of stenosis severity on multislice computed tomography in comparison with quantitative coronary angiography (QCA).
Background: Limited information is available on quantification of coronary stenosis, and previous attempts using semiautomated approaches have been suboptimal.
Methods: In patients who had undergone 64-slice computed tomography and invasive coronary angiography, the most severe lesion on QCA was quantified per coronary artery using quantitative coronary computed tomography (QCCTA) software. Additionally, visual grading of stenosis severity using a binary approach (50% stenosis as a cutoff) was performed. Diameter stenosis (percentage) was obtained from detected lumen contours at the minimal lumen area, and corresponding reference diameter values were obtained from an automatic trend analysis of the vessel areas within the artery.
Results: One hundred patients (53 men; 59.8 ± 8.0 years) were evaluated, and 282 (94%) vessels were analyzed. Good correlations for diameter stenosis were observed for vessel-based (n = 282; r = 0.83; p < 0.01) and patient-based (n = 93; r = 0.86; p < 0.01) analyses. Mean differences between QCCTA and QCA were –3.0% ± 12.3% and –6.2% ± 12.4%. Furthermore, good agreement was observed between QCCTA and QCA for semiquantitative assessment of diameter stenosis (accuracy of 95%). Diagnostic accuracy for assessment of 50% diameter stenosis was higher using QCCTA compared with visual analysis (95% vs. 87%; p = 0.08). Moreover, a significantly higher positive predictive value was observed with QCCTA when compared with visual analysis (100% vs. 78%; p < 0.05). Although the visual approach showed a reduced diagnostic accuracy for data sets with moderate image quality, QCCTA performed equally well in patients with moderate or good image quality. However, in data sets with good image quality, QCCTA tended to have a reduced sensitivity compared with visual analysis.
Conclusions: Good correlations were found for quantification of stenosis severity between QCCTA and QCA. QCCTA showed an improved positive predictive value when compared with visual analysis.
Key Words: computed tomography automated quantification diameter stenosis
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Abbreviations and Acronyms
| | CAD = coronary artery disease | | IVUS = intravascular ultrasound | | MCA = model-guided minimum cost approach | | MLA = minimal lumen area | | MPR = multiplanar reformatted | | MSCT = multislice computed tomography | | QCCTA = quantitative coronary computed tomography angiography | | QCA = quantitative coronary angiography |
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