Role of Computed Tomography Perfusion in Identification of Acute Lacunar Stroke Syndromes.


Journal

Stroke
ISSN: 1524-4628
Titre abrégé: Stroke
Pays: United States
ID NLM: 0235266

Informations de publication

Date de publication:
01 2021
Historique:
entrez: 28 12 2020
pubmed: 29 12 2020
medline: 20 4 2021
Statut: ppublish

Résumé

Lacunar syndromes correlate with a lacunar stroke on imaging in 50% to 60% of cases. Computed tomography perfusion (CTP) is becoming the preferred imaging modality for acute stroke triage. We aimed to estimate the sensitivity, specificity, and predictive values for noncontrast computed tomography and CTP in lacunar syndromes, and for cortical, subcortical, and posterior fossa regions. A retrospective analysis of confirmed ischemic stroke patients who underwent acute CTP and follow-up magnetic resonance imaging between 2010 and 2018 was performed. Brain noncontrast computed tomography and CTP were assessed independently by 2 stroke neurologists. Receiver operating characteristic curve analysis was performed to estimate sensitivity, specificity, and area under the curve (AUC) for the detection of strokes in patients with lacunar syndromes using different CTP maps. We found 106 clinical lacunar syndromes, but on diffusion-weighted imaging, these consisted of 59 lacunar, 33 cortical, and 14 posterior fossa strokes. The discrimination of ischemia identification was very poor using noncontrast computed tomography in all 3 regions, but good for cortical (AUC, 0.82) and poor for subcortical and posterior regions (AUCs, 0.55 and 0.66) using automated core-penumbra maps. The addition of delay time and mean transient time maps substantially increased subcortical (AUC, 0.80) and slightly posterior stroke detection (AUC, 0.69). Analysis of mean transient time and delay time maps in combination with core-penumbra maps improves detection of subcortical and posterior strokes.

Sections du résumé

BACKGROUND AND PURPOSE
Lacunar syndromes correlate with a lacunar stroke on imaging in 50% to 60% of cases. Computed tomography perfusion (CTP) is becoming the preferred imaging modality for acute stroke triage. We aimed to estimate the sensitivity, specificity, and predictive values for noncontrast computed tomography and CTP in lacunar syndromes, and for cortical, subcortical, and posterior fossa regions.
METHODS
A retrospective analysis of confirmed ischemic stroke patients who underwent acute CTP and follow-up magnetic resonance imaging between 2010 and 2018 was performed. Brain noncontrast computed tomography and CTP were assessed independently by 2 stroke neurologists. Receiver operating characteristic curve analysis was performed to estimate sensitivity, specificity, and area under the curve (AUC) for the detection of strokes in patients with lacunar syndromes using different CTP maps.
RESULTS
We found 106 clinical lacunar syndromes, but on diffusion-weighted imaging, these consisted of 59 lacunar, 33 cortical, and 14 posterior fossa strokes. The discrimination of ischemia identification was very poor using noncontrast computed tomography in all 3 regions, but good for cortical (AUC, 0.82) and poor for subcortical and posterior regions (AUCs, 0.55 and 0.66) using automated core-penumbra maps. The addition of delay time and mean transient time maps substantially increased subcortical (AUC, 0.80) and slightly posterior stroke detection (AUC, 0.69).
CONCLUSIONS
Analysis of mean transient time and delay time maps in combination with core-penumbra maps improves detection of subcortical and posterior strokes.

Identifiants

pubmed: 33370180
doi: 10.1161/STROKEAHA.120.030455
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

339-343

Auteurs

Carlos Garcia-Esperon (C)

Department of Neurology, John Hunter Hospital, University of Newcastle, Australia (C.G.-E., F.M., N.J.S.).
Hunter Medical Research Institute and University of Newcastle, Australia (C.G.-E., F.M., T.L., C.R.L., N.J.S., M.W.P.).

Milanka Visser (M)

Department of Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia (M.V., L.C., A.B., M.W.P.).

Leonid Churilov (L)

Department of Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia (M.V., L.C., A.B., M.W.P.).
Department of Medicine, Austin Health, Melbourne Medical School, Austin Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Victoria, Australia (L.C.).

Ferdinand Miteff (F)

Department of Neurology, John Hunter Hospital, University of Newcastle, Australia (C.G.-E., F.M., N.J.S.).
Hunter Medical Research Institute and University of Newcastle, Australia (C.G.-E., F.M., T.L., C.R.L., N.J.S., M.W.P.).

Andrew Bivard (A)

Department of Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia (M.V., L.C., A.B., M.W.P.).

Thomas Lillicrap (T)

Hunter Medical Research Institute and University of Newcastle, Australia (C.G.-E., F.M., T.L., C.R.L., N.J.S., M.W.P.).

Christopher R Levi (CR)

Hunter Medical Research Institute and University of Newcastle, Australia (C.G.-E., F.M., T.L., C.R.L., N.J.S., M.W.P.).
Maridulu budyari gumal, The Sydney Partnership for Health Education Research & Enterprise (SPHERE), University of New South Wales, Australia (C.R.L.).

Neil J Spratt (NJ)

Department of Neurology, John Hunter Hospital, University of Newcastle, Australia (C.G.-E., F.M., N.J.S.).
Hunter Medical Research Institute and University of Newcastle, Australia (C.G.-E., F.M., T.L., C.R.L., N.J.S., M.W.P.).

Mark W Parsons (MW)

Hunter Medical Research Institute and University of Newcastle, Australia (C.G.-E., F.M., T.L., C.R.L., N.J.S., M.W.P.).
Department of Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, Australia (M.V., L.C., A.B., M.W.P.).

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