dc.contributor.author | Kakkos, Stavros K. | en |
dc.contributor.author | Stevens, J. M. | en |
dc.contributor.author | Nicolaïdes, Andrew N. | en |
dc.contributor.author | Kyriacou, Efthyvoulos C. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Geroulakos, George | en |
dc.contributor.author | Thomas, Dominique | en |
dc.creator | Kakkos, Stavros K. | en |
dc.creator | Stevens, J. M. | en |
dc.creator | Nicolaïdes, Andrew N. | en |
dc.creator | Kyriacou, Efthyvoulos C. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Geroulakos, George | en |
dc.creator | Thomas, Dominique | en |
dc.date.accessioned | 2019-11-13T10:40:31Z | |
dc.date.available | 2019-11-13T10:40:31Z | |
dc.date.issued | 2007 | |
dc.identifier.issn | 1078-5884 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54162 | |
dc.description.abstract | Objectives: The aim of our study was to determine the association between objective, computerised texture analysis of carotid plaque ultrasonic images and embolic CT-brain infarction in patients presenting with hemispheric neurological symptoms. Design: Cross-sectional study in patients with 50%-99% (ECST) carotid stenosis. Patients and Methods: Carotid plaque ultrasonic images (n = 54, 26 with TIAs and 28 with stroke) obtained during carotid ultrasound were normalised and standardised for resolution and subsequently assessed visually for the presence of discrete echogenic or juxtaluminal echolucent components and overall echogenicity (plaque type). Using computer software, 51 histogram/textural features of the plaque outlines were calculated. Factor analysis was subsequently applied to eliminate redundant variables. Small cortical, large cortical and discrete subcortical infarcts on CT-brain scan were considered as being embolic. Results: Twenty-five cases (46%) had embolic infarcts. On logistic regression, grey-scale median (GSM), a measure of echolucency, spatial grey level dependence matrices (SGLDM) correlation and SGLDM information measure of correlation-1, measures of homogeneity were significant (p < 0.05), but not grey level runlength statistics (RUNL) Run Percentage (RP), stenosis severity, type of symptoms or echolucent juxtaluminal components. Using ROC curves methodology, SGLDM information measure of correlation-1 improved the value of GSM in distinguishing embolic from non-embolic CT-brain infarction. Conclusion: Computerised texture analysis of ultrasonic images of symptomatic carotid plaques can identify those that are associated with brain infarction, improving the results achieved by GSM alone. This methodology could be applied to prospective natural history studies of symptomatic patients not operated on or randomised trials of patients undergoing carotid angioplasty and stenting in order to identify high-risk subgroups for cerebral infarction. © 2006. | en |
dc.source | European Journal of Vascular and Endovascular Surgery | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-33847401040&doi=10.1016%2fj.ejvs.2006.10.018&partnerID=40&md5=38eb935cbec337cccc55bea4aaba36e3 | |
dc.subject | article | en |
dc.subject | Algorithms | en |
dc.subject | human | en |
dc.subject | Humans | en |
dc.subject | controlled study | en |
dc.subject | Cross-Sectional Studies | en |
dc.subject | major clinical study | en |
dc.subject | computer program | en |
dc.subject | Predictive Value of Tests | en |
dc.subject | priority journal | en |
dc.subject | correlation analysis | en |
dc.subject | logistic regression analysis | en |
dc.subject | Logistic Models | en |
dc.subject | neurologic disease | en |
dc.subject | Reproducibility of Results | en |
dc.subject | Risk Assessment | en |
dc.subject | disease severity | en |
dc.subject | Severity of Illness Index | en |
dc.subject | brain cortex | en |
dc.subject | image analysis | en |
dc.subject | echography | en |
dc.subject | brain tomography | en |
dc.subject | Software | en |
dc.subject | Ultrasound | en |
dc.subject | Sensitivity and Specificity | en |
dc.subject | stroke | en |
dc.subject | transient ischemic attack | en |
dc.subject | calculation | en |
dc.subject | Texture | en |
dc.subject | atherosclerotic plaque | en |
dc.subject | receiver operating characteristic | en |
dc.subject | physical parameters | en |
dc.subject | carotid artery disease | en |
dc.subject | stenosis | en |
dc.subject | Image Interpretation, Computer-Assisted | en |
dc.subject | Carotid Stenosis | en |
dc.subject | Gray scale echography | en |
dc.subject | ROC Curve | en |
dc.subject | Ultrasonography, Doppler, Duplex | en |
dc.subject | histogram | en |
dc.subject | brain embolism | en |
dc.subject | brain infarction | en |
dc.subject | Carotid arteries | en |
dc.subject | Cerebral infarction | en |
dc.subject | Factor Analysis, Statistical | en |
dc.subject | factorial analysis | en |
dc.subject | Intracranial Embolism | en |
dc.subject | radiological parameters | en |
dc.subject | Tomography, X-Ray Computed | en |
dc.title | Texture Analysis of Ultrasonic Images of Symptomatic Carotid Plaques can Identify Those Plaques Associated with Ipsilateral Embolic Brain Infarction | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1016/j.ejvs.2006.10.018 | |
dc.description.volume | 33 | |
dc.description.issue | 4 | |
dc.description.startingpage | 422 | |
dc.description.endingpage | 429 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :44</p> | en |
dc.source.abbreviation | Eur.J.Vasc.Endovasc.Surg. | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Kyriacou, Efthyvoulos C. [0000-0002-4589-519X] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0002-4589-519X | |