BACKGROUND: Multidetector computed tomography-coronary angiography allows quantification of coronary stenosis with a high level of accuracy; however, the inherent inaccuracy of visual score still remains. Computed quantitative vessel analysis systems (quantitative computed tomographic angiography [QCTA]) aim to overcome this limitation. The aim of our study was to evaluate the accuracy of QCTA in comparison with quantitative coronary angiography (QCA) and visual score using the QCA. MATERIALS AND METHODS: Two operators visually scored 30 consecutive patients referred for multidetector computed tomography-coronary angiography to assess stenotic segments according to a modified 17-segment American Heart Association classification model. Coronary angiography was performed within 1 week. The degree of stenosis was classified as 0%, lower than 20% (wall irregularities), lower than 50% (without significant disease), and higher than 50% (significant disease). Each segment was then analyzed using electronic calipers of the QCTA system. Data were compared with QCA results. Each segment was finally classified as fibrofatty, mixed, and calcified. Comparisons between QCTA results, visual score, and QCA were performed by means of Spearman rank correlation. Interobserver variability is calculated using κ statistics. RESULTS: From a total of 870 segments, 69 were diseased. Interobserver agreement between the 2 operators resulted very high (κ = 0.97). A good correlation was found between visual score and QCA (ρ = 0.932, P < 0.0001) and between visual score and QCTA (ρ = 0.845, P < 0.0001). A moderate correlation was found between QCTA and QCA (ρ = 0.810, P < 0.0001). CONCLUSIONS: The accuracy of QCTA is comparable with that of QCA and visual score especially in noncalcified vessels. Editing of the vessel contours in case of calcified vessels is helpful in correctly estimating the right percentage of stenosis.
BACKGROUND: Multidetector computed tomography-coronary angiography allows quantification of coronary stenosis with a high level of accuracy; however, the inherent inaccuracy of visual score still remains. Computed quantitative vessel analysis systems (quantitative computed tomographic angiography [QCTA]) aim to overcome this limitation. The aim of our study was to evaluate the accuracy of QCTA in comparison with quantitative coronary angiography (QCA) and visual score using the QCA. MATERIALS AND METHODS: Two operators visually scored 30 consecutive patients referred for multidetector computed tomography-coronary angiography to assess stenotic segments according to a modified 17-segment American Heart Association classification model. Coronary angiography was performed within 1 week. The degree of stenosis was classified as 0%, lower than 20% (wall irregularities), lower than 50% (without significant disease), and higher than 50% (significant disease). Each segment was then analyzed using electronic calipers of the QCTA system. Data were compared with QCA results. Each segment was finally classified as fibrofatty, mixed, and calcified. Comparisons between QCTA results, visual score, and QCA were performed by means of Spearman rank correlation. Interobserver variability is calculated using κ statistics. RESULTS: From a total of 870 segments, 69 were diseased. Interobserver agreement between the 2 operators resulted very high (κ = 0.97). A good correlation was found between visual score and QCA (ρ = 0.932, P < 0.0001) and between visual score and QCTA (ρ = 0.845, P < 0.0001). A moderate correlation was found between QCTA and QCA (ρ = 0.810, P < 0.0001). CONCLUSIONS: The accuracy of QCTA is comparable with that of QCA and visual score especially in noncalcified vessels. Editing of the vessel contours in case of calcified vessels is helpful in correctly estimating the right percentage of stenosis.
Authors: R Malagò; A Pezzato; C Barbiani; W Mantovani; G Caliari; U Alfonsi; D Tavella; R Pozzi Mucelli Journal: Radiol Med Date: 2011-09-02 Impact factor: 3.469
Authors: Fang Yuan; Liang Guo; Kyoung-Ha Park; John R Woollard; Kwon Taek-Geun; Kai Jiang; Tamene Melkamu; Bin Zang; Samantha L Smith; Scott C Fahrenkrug; Frank D Kolodgie; Amir Lerman; Renu Virmani; Lilach O Lerman; Daniel F Carlson Journal: J Am Heart Assoc Date: 2018-03-23 Impact factor: 5.501