Literature DB >> 19683886

128-detector-row computed tomography coronary angiography assessing differences in morphology and distribution of atherosclerotic plaques between patients with and without pre-test probability of significant coronary artery disease.

O Lazoura1, M Vlychou, K Vassiou, A Kelekis, T Kanavou, P Thriskos, I V Fezoulidis.   

Abstract

OBJECTIVES: To evaluate and compare morphology, distribution and orientation of atherosclerotic plaques at the coronary arteries between patients with low and intermediate pre-test probability of significant coronary artery disease (CAD) by non-invasive coronary angiography using 128-Multi Detector Computed Tomography (MDCT).
MATERIALS AND METHODS: The study included 120 patients divided into two groups according to their clinical pre-test probability of having significant CAD: 38 patients (group A) with intermediate pre-test probability and 82 patients (group B) with low pre-test probability of significant CAD. Atherosclerotic plaques were characterized according to their morphology, distribution and orientation.
RESULTS: A total of 482 plaques were analyzed. In group A, we found statistically significant higher percentages of RCA plaques (p=0.0005), of concentric (p<0.0001) and non-branching (p=0.013) plaques, of myocardial plaques (p=0.029), of plaques in distal RCA (p=0.0009) and distal LAD (p=0.001). In group B, we found statistically significant higher percentages of LAD plaques (p<0.0001), of eccentric (p<0.0001) and branching (p=0.013) plaques, of lateral plaques (p=0.012), of Medina 1.0.0 (p=0.0069), 0.1.0 (p=0.022) and 1.1.1 (p=0.0068) branching plaques, and of plaques in proximal LAD (p=0.02).
CONCLUSION: 128-MDCT coronary angiography can provide important information on morphology and distribution of atherosclerotic plaques and may in the future play a potential role in patient management.
Copyright © 2009 Elsevier Ireland Ltd. All rights reserved.

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Year:  2009        PMID: 19683886     DOI: 10.1016/j.ejrad.2009.07.019

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  1 in total

1.  A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm.

Authors:  Ali A Rostam-Alilou; Marziyeh Safari; Hamid R Jarrah; Ali Zolfagharian; Mahdi Bodaghi
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-08-10       Impact factor: 3.421

  1 in total

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