Literature DB >> 19628937

Computer-assisted analysis of heterogeneity on B-mode imaging predicts instability of asymptomatic carotid plaque.

Hiroyuki Hashimoto1, Masafumi Tagaya, Hitoshi Niki, Hideki Etani.   

Abstract

BACKGROUND: Computerized assessment of plaque echogenicity by B-mode ultrasonography has demonstrated that the gray-scale median (GSM) pixel intensity of the entire plaque predicts future ischemic stroke in patients with symptomatic carotid stenosis, but not those with asymptomatic stenosis. This study investigated whether plaque heterogeneity (i.e., the distribution of pixel intensities) could predict the instability of asymptomatic plaque.
METHODS: By comparison with carotid endarterectomy specimens and the GSM values of known tissues on B-mode images, the GSM values for blood, lipid, muscle/fibrous tissue, and calcification were determined. Then we estimated the percent area of each tissue component for 297 asymptomatic plaques causing 40-99% carotid artery stenosis in 250 patients, and monitored the incidence of atherothrombotic cerebral infarction due to carotid stenosis during follow-up.
RESULTS: Eight infarcts occurred during a follow-up period of 22 +/- 15 months. Plaques in the top tertile for the percent area of lipid-like echogenicity (p < 0.05) and in the lowest tertile for calcification (p = 0.06) showed an association with future infarction according to Kaplan-Meier analysis. This association remained significant after adjustment for the severity of carotid stenosis (hazard ratio 4.4 for lipid-like and 0.24 for calcification-like component, both p < 0.05) according to Cox proportional hazards analysis.
CONCLUSIONS: The distribution of pixel intensities in carotid plaque on B-mode ultrasonography can be employed to predict instability of asymptomatic plaque and possibly to select patients for interventional procedures. A large-scale investigation will be needed to confirm that estimating the percentage of plaque components relative to the total plaque area can predict ischemic stroke. Copyright 2009 S. Karger AG, Basel.

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Year:  2009        PMID: 19628937     DOI: 10.1159/000229554

Source DB:  PubMed          Journal:  Cerebrovasc Dis        ISSN: 1015-9770            Impact factor:   2.762


  5 in total

1.  Detection of Symptomatic Carotid Plaque Using Source Data from MR and CT Angiography: A Correlative Study.

Authors:  Ajay Gupta; Hediyeh Baradaran; Edward E Mtui; Hooman Kamel; Ankur Pandya; Ashley Giambrone; Costantino Iadecola; Pina C Sanelli
Journal:  Cerebrovasc Dis       Date:  2015-02-17       Impact factor: 2.762

2.  Assessment of vulnerable and unstable carotid atherosclerotic plaques on endarterectomy specimens.

Authors:  Doina Butcovan; Veronica Mocanu; Dana Baran; Diana Ciurescu; Grigore Tinica
Journal:  Exp Ther Med       Date:  2016-02-19       Impact factor: 2.447

3.  A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound.

Authors:  Karim Lekadir; Alfiia Galimzianova; Angels Betriu; Maria Del Mar Vila; Laura Igual; Daniel L Rubin; Elvira Fernandez; Petia Radeva; Sandy Napel
Journal:  IEEE J Biomed Health Inform       Date:  2016-11-22       Impact factor: 5.772

4.  Atherosclerotic Calcification Detection: A Comparative Study of Carotid Ultrasound and Cone Beam CT.

Authors:  Fisnik Jashari; Pranvera Ibrahimi; Elias Johansson; Jan Ahlqvist; Conny Arnerlöv; Maria Garoff; Eva Levring Jäghagen; Per Wester; Michael Y Henein
Journal:  Int J Mol Sci       Date:  2015-08-21       Impact factor: 5.923

Review 5.  A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.

Authors:  Mainak Biswas; Luca Saba; Tomaž Omerzu; Amer M Johri; Narendra N Khanna; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Antonella Balestrieri; Petros P Sfikakis; Athanasios Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Raghu Kolluri; Aditya Sharma; Vijay Viswanathan; Zoltan Ruzsa; Andrew Nicolaides; Jasjit S Suri
Journal:  J Digit Imaging       Date:  2021-06-02       Impact factor: 4.903

  5 in total

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