RELATIONSHIP OF GENDER TO CORONARY PLAQUE TYPE AND DISTRIBUTION: ANALYSIS BY 64-DETECTOR COMPUTED TOMOGRAPHIC ANGIOGRAPHY.

2007 ◽  
Vol 55 (1) ◽  
pp. S294
Author(s):  
L. Lubarsky ◽  
M. Prakash ◽  
J. Gottsegen ◽  
N. Coplan ◽  
H. Hecht
Keyword(s):  
Coronary Plaque ◽  
Computed Tomographic Angiography ◽  
Distribution Analysis ◽  
Computed Tomographic ◽  
Plaque Type ◽  
Tomographic Angiography ◽  
Relationship Of

scholarly journals A New Approach in Risk Stratification by Coronary CT Angiography

Scientifica ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Rine Nakanishi ◽  
Matthew J. Budoff
Keyword(s):  
Risk Stratification ◽  
Invasive Coronary Angiography ◽  
Coronary Plaque ◽  
Computed Tomographic Angiography ◽  
Plaque Burden ◽  
Cardiovascular Risks ◽  
Computed Tomographic ◽  
Coronary Ct ◽  
Hemodynamic Information ◽  
Tomographic Angiography

For a decade, coronary computed tomographic angiography (CCTA) has been used as a promising noninvasive modality for the assessment of coronary artery disease (CAD) as well as cardiovascular risks. CCTA can provide more information incorporating the presence, extent, and severity of CAD; coronary plaque burden; and characteristics that highly correlate with those on invasive coronary angiography. Moreover, recent techniques of CCTA allow assessing hemodynamic significance of CAD. CCTA may be potentially used as a substitute for other invasive or noninvasive modalities. This review summarizes risk stratification by anatomical and hemodynamic information of CAD, coronary plaque characteristics, and burden observed on CCTA.

scholarly journals Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Open Heart ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. e001832
Author(s):  
Rebecca Jonas ◽  
James Earls ◽  
Hugo Marques ◽  
Hyuk-Jae Chang ◽  
Jung Hyun Choi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Angiography ◽  
Computed Tomographic Angiography ◽  
Lesion Length ◽  
Coronary Computed Tomographic Angiography ◽  
Younger Patients ◽  
Computed Tomographic ◽  
Calcified Plaque ◽  
Tomographic Angiography ◽  
Relationship Of

ObjectiveThe study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).MethodsThis is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years.ResultsThe cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p<0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients.ConclusionAI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.

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