Multidetector computed tomography evaluation of coronary plaque morphology in patients with stable angina

Utsunomiya, Daisuke; Fukunaga, Takashi; Oda, Shigeto; Awai, Kazuo; Nakaura, Takeshi; Urata, Joji; Yamashita, Yasuyuki

doi:10.1007/s00380-010-0074-4

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…Whereas in other studies the calcification process was investigated in vitro and in animal models (rats) being fed with vitamin D and nicotine [6,23], the present study was performed in subjects with normal renal function and balanced calciumphosphate homeostasis. Another difference is the technique applied for the evaluation of calcification processes, which was atomic absorption spectrophotometry in invitro studies [6,21] and 64-MSCT imaging in our study, which is the gold standard technique for the non-invasive evaluation of the CACS in various clinical settings [24][25][26]. Our study is limited by the small samples size.…”

Section: Discussionmentioning

confidence: 74%

Association of midregional proadrenomedullin with coronary artery stenoses, soft atherosclerotic plaques and coronary artery calcium

et al. 2011

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Midregional proadrenomedullin (MR-proADM) is elevated in patients with heart failure and myocardial infarction. The aim of this study was to evaluate the association of MR-proADM with the grade of coronary artery stenosis, presence of coronary artery soft plaques and coronary artery calcification score (CACS), determined by 64-multislice computed tomography (MSCT) in patients without known prior cardiovascular disease. This retrospective study included 107 patients undergoing MSCT for confirmation (or exclusion) of coronary artery disease. MR-proADM levels were measured in all patients. The assessment of coronary artery stenoses, CACS and soft coronary plaques was made by MSCT using known criteria. The MR-proADM [median (25th-75th percentiles)] level was 0.33 (0.21-0.43) nmol/l. The MR-proADM level was 0.28 (0.22-0.40) nmol/l in patients with coronary stenoses ≥50% (n = 23) versus 0.33 (0.27-0.40) nmol/l in patients with coronary stenoses <50% (n = 83, P = 0.59), 0.33 (0.26-0.40) nmol/l in patients with soft plaques (n = 56) versus 0.33 (0.25-0.41) nmol/l in patients without soft plaques (n = 50, P = 0.73) and 0.33 (0.25-0.39) nmol/l in patients with CACS <200 (n = 81) versus 0.32 (0.26-0.44) nmol/l in patients with CACS ≥200 (n = 26, P = 0.77). In multivariate analysis, the MR-proADM level was a significant correlate of coronary artery stenoses [odds ratio (OR) = 0.93; 95% confidence interval (CI) 0.86-0.99; P = 0.026] and soft plaques (OR = 0.94; 95% CI 0.90-0.99; P = 0.015) but not of CACS (OR = 0.98; 95% CI 0.93-1.03; P = 0.36). A decreased MR-proADM level is an independent correlate of the presence of coronary artery disease and of soft atherosclerotic plaques. Patients with decreased MR-proADM levels may need invasive examinations to diagnose more severe forms of coronary artery disease.

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Section: Discussionmentioning

confidence: 74%

Association of midregional proadrenomedullin with coronary artery stenoses, soft atherosclerotic plaques and coronary artery calcium

et al. 2011

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“…Therefore, characterization of a coronary plaque causing mild-to-moderate stenosis is a clinically important role of coronary CTA for appropriate patient management. The CT number of a soft plaque (unstable plaque) is approximately ≤40 HU (32) and that of a fibrous plaque (stable plaque) is 80–100 HU (7). Our phantom study results (50% stenosis model with 40-HU plaque) suggested that the non-calcified plaque changed subtype from soft/lipid-rich to fibrotic with FBP and AIDR3D.…”

Section: Discussionmentioning

confidence: 99%

Improved Estimation of Coronary Plaque and Luminal Attenuation Using a Vendor-specific Model-based Iterative Reconstruction Algorithm in Contrast-enhanced CT Coronary Angiography

et al. 2017

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Rationale and Objectives To investigate the stabilities of plaque attenuation and coronary lumen for different plaque types, stenotic degrees, lumen densities, and reconstruction methods using coronary vessel phantoms and the visualization of coronary plaques in clinical patients through coronary CT angiography. Materials and Methods We performed 320-detector volume scanning of vessel tubes with stenosis and a tube without stenosis using three types of plaque CT numbers. The stenotic degrees were 50% and 75%. Images were reconstructed with filtered back projection (FBP) and two types of iterative reconstructions (AIDR3D and FIRST), with stenotic CT number of approximately 40, 80, and 150 HU, respectively. In each case, the tubing of the coronary vessel was filled with diluted contrast material and distilled water to reach the target lumen CT numbers of approximately 350 HU and 450 HU, and 0 HU, respectively. Peak lumen and plaque CT numbers were measured to calculate the lumen–plaque contrast. In addition, we retrospectively evaluated the image quality with regard to coronary arterial lumen and the plaque in ten clinical patients on a four-point scale. Results At 50% stenosis, the plaque CT number with contrast enhancement increased for FBP and AIDR3D and the difference in the plaque CT number with and without contrast enhancement was 15–44 HU for FBP and 10–31 HU for AIDR3D. However, the plaque CT number for FIRST had a smaller variation and the difference with and without contrast enhancement was −12–8 HU. The visual evaluation score for the vessel lumen was 2.8 ± 0.6, 3.5 ± 0.5, and 3.7 ± 0.5 for FBP, AIDR3D, and FIRST, respectively. Conclusion The FIRST method controls the increase in plaque density and the lumen–plaque contrast. Consequently, it improves the visualization of coronary plaques in coronary CT angiography.

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“…In July 2011, we reported the coronary CT angiography (CCTA) imaging features of ischemia-related coronary plaques in patients with stable angina on 64-row multidetector CT [1]. We used the univariate and multivariate logistic regression analysis to identify which clinical characteristics and CT imaging findings were useful to differentiate ischemia-related lesions from nonischemia-relate lesions.…”

Section: Short Commentarymentioning

confidence: 99%

Artificial Intelligence for the Interpretation of Coronary Computed Tomography Angiography: Can Machine Learning Improve Diagnostic Performance?

Utsunomiya¹,

Nakaura²,

Oda³

2016

J Clin Exp Cardiolog

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Recent development of artificial intelligence (AI) and machine learning system has a potential to improve the clinical diagnosis of coronary artery disease. Coronary computed tomography angiography (CCTA) provides important information of coronary arteries: i.e., stenosis severity, lesion length, plaque attenuation, and degree of calcium deposition. However, the comprehensive analysis of these factors may be difficult. We analyzed patient characteristics and CCTA findings of 56 patients. We used AI (a random forest) to identify the ischemia-related lesions, and compare the diagnostic performance of a random forest and a logistic regression analysis. By the analysis of a random forest, the area under the curve was increased from 0.89 (a logistic regression analysis) to 0.95 (a random forest). Machine learning models can be helpful for the interpretation of CCTA for detecting ischemia-related coronary lesions.

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Multidetector computed tomography evaluation of coronary plaque morphology in patients with stable angina

Cited by 13 publications

References 33 publications

Association of midregional proadrenomedullin with coronary artery stenoses, soft atherosclerotic plaques and coronary artery calcium

Association of midregional proadrenomedullin with coronary artery stenoses, soft atherosclerotic plaques and coronary artery calcium

Improved Estimation of Coronary Plaque and Luminal Attenuation Using a Vendor-specific Model-based Iterative Reconstruction Algorithm in Contrast-enhanced CT Coronary Angiography

Artificial Intelligence for the Interpretation of Coronary Computed Tomography Angiography: Can Machine Learning Improve Diagnostic Performance?

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