Quantitative assessment of carotid plaque morphology (geometry and tissue composition) using computed tomography angiography.


Journal

Journal of vascular surgery
ISSN: 1097-6809
Titre abrégé: J Vasc Surg
Pays: United States
ID NLM: 8407742

Informations de publication

Date de publication:
09 2019
Historique:
received: 28 09 2018
accepted: 26 11 2018
pubmed: 10 3 2019
medline: 30 1 2020
entrez: 10 3 2019
Statut: ppublish

Résumé

Quantification of carotid plaque morphology (geometry and tissue composition) may help stratify risk for future stroke and assess plaque progression or regression in response to medical risk factor modification. We assessed the feasibility and reliability of morphologic measurements of carotid plaques using computed tomography angiography (CTA) and determined the minimum detectable change in plaque features by this approach. CTA images of both carotid arteries in 50 patients were analyzed by two observers using a semiautomatic image analysis program, yielding 93 observations per user (seven arteries were excluded because of prior stenting). One observer repeated the analyses 4 weeks later. Measurements included total plaque volume; percentage stenosis (by diameter and area); and tissue composition for calcium, lipid-rich necrotic core (LRNC), and intraplaque hemorrhage (IPH). Reliability of measurements was assessed by intraclass and interclass correlation and Bland-Altman plots. Dice similarity coefficient (DSC) and modified Hausdorff distance (MHD) assessed reliability of geometric shape measurements. We additionally computed the minimum amount of change in these features detectable by our approach. The cohort was 51% male (mean age, 70.1 years), and 56% had a prior stroke. The mean (± standard deviation) plaque volume was 837.3 ± 431.3 mm Carotid plaque geometry (total volume, diameter stenosis, and area stenosis) and tissue composition (calcium, LRNC, and IPH) are measured reliably from clinical CTA images using a semiautomatic image analysis program. The minimum change in plaque volume detectable is ∼4% if the same observer makes both measurements and ∼10% for different observers. Small changes in plaque composition can also be detected reliably. This approach can facilitate longitudinal studies for identifying high-risk plaque features and for quantifying plaque progression or regression after treatment.

Identifiants

pubmed: 30850296
pii: S0741-5214(19)30141-7
doi: 10.1016/j.jvs.2018.11.050
pmc: PMC7034302
mid: NIHMS1068016
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

858-868

Subventions

Organisme : NIA NIH HHS
ID : P30 AG028747
Pays : United States

Informations de copyright

Copyright © 2019 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

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Auteurs

Matthew T Chrencik (MT)

Department of Vascular Surgery, University of Maryland School of Medicine, Baltimore, Md; Vascular Service, Veterans Affairs Medical Center, Baltimore, Md.

Amir A Khan (AA)

Department of Bioengineering, George Mason University, Fairfax, Va.

Lauren Luther (L)

Department of Vascular Surgery, University of Maryland School of Medicine, Baltimore, Md; Vascular Service, Veterans Affairs Medical Center, Baltimore, Md.

Laila Anthony (L)

Department of Vascular Surgery, University of Maryland School of Medicine, Baltimore, Md; Vascular Service, Veterans Affairs Medical Center, Baltimore, Md.

John Yokemick (J)

Department of Vascular Surgery, University of Maryland School of Medicine, Baltimore, Md; Vascular Service, Veterans Affairs Medical Center, Baltimore, Md.

Jigar Patel (J)

Imaging Service, VA Maryland Health Care System, Baltimore, Md.

John D Sorkin (JD)

Baltimore VA Medical Center Geriatric Research, Education, and Clinical Center, Baltimore Veterans Affairs Medical Center, Baltimore, Md; Claude D. Pepper Older Americans Independence Center, University of Maryland School of Medicine, Baltimore, Md.

Siddhartha Sikdar (S)

Department of Bioengineering, George Mason University, Fairfax, Va.

Brajesh K Lal (BK)

Department of Vascular Surgery, University of Maryland School of Medicine, Baltimore, Md; Vascular Service, Veterans Affairs Medical Center, Baltimore, Md. Electronic address: blal@som.umaryland.edu.

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