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Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography

PURPOSE: Computed tomography angiography (CTA) is routinely used to detect large-vessel occlusion (LVO) in patients with suspected acute ischemic stroke; however, visual analysis is time consuming and prone to error. To evaluate solutions to support imaging triage, we tested performance of automated...

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Autores principales: Reidler, Paul, Stueckelschweiger, Lena, Puhr-Westerheide, Daniel, Feil, Katharina, Kellert, Lars, Dimitriadis, Konstantinos, Tiedt, Steffen, Herzberg, Moriz, Rémi, Jan, Liebig, Thomas, Fabritius, Matthias P., Kunz, Wolfgang G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463515/
https://www.ncbi.nlm.nih.gov/pubmed/32939563
http://dx.doi.org/10.1007/s00062-020-00956-5
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author Reidler, Paul
Stueckelschweiger, Lena
Puhr-Westerheide, Daniel
Feil, Katharina
Kellert, Lars
Dimitriadis, Konstantinos
Tiedt, Steffen
Herzberg, Moriz
Rémi, Jan
Liebig, Thomas
Fabritius, Matthias P.
Kunz, Wolfgang G.
author_facet Reidler, Paul
Stueckelschweiger, Lena
Puhr-Westerheide, Daniel
Feil, Katharina
Kellert, Lars
Dimitriadis, Konstantinos
Tiedt, Steffen
Herzberg, Moriz
Rémi, Jan
Liebig, Thomas
Fabritius, Matthias P.
Kunz, Wolfgang G.
author_sort Reidler, Paul
collection PubMed
description PURPOSE: Computed tomography angiography (CTA) is routinely used to detect large-vessel occlusion (LVO) in patients with suspected acute ischemic stroke; however, visual analysis is time consuming and prone to error. To evaluate solutions to support imaging triage, we tested performance of automated analysis of CTA source images (CTASI) at identifying patients with LVO. METHODS: Stroke patients with LVO were selected from a prospectively acquired cohort. A control group was selected from consecutive patients with clinically suspected stroke without signs of ischemia on CT perfusion (CTP) or infarct on follow-up. Software-based automated segmentation and Hounsfield unit (HU) measurements were performed on CTASI for all regions of the Alberta Stroke Program Early CT score (ASPECTS). We derived different parameters from raw measurements and analyzed their performance to identify patients with LVO using receiver operating characteristic curve analysis. RESULTS: The retrospective analysis included 145 patients, 79 patients with LVO stroke and 66 patients without stroke. The parameters hemispheric asymmetry ratio (AR), ratio between highest and lowest regional AR and M2-territory AR produced area under the curve (AUC) values from 0.95–0.97 (all p < 0.001) for detecting presence of LVO in the total population. Resulting sensitivity (sens)/specificity (spec) defined by the Youden index were 0.87/0.97–0.99. Maximum sens/spec defined by the specificity threshold ≥0.70 were 0.91–0.96/0.77–0.83. Performance in a small number of patients with isolated M2 occlusion was lower (AUC: 0.72–0.85). CONCLUSION: Automated attenuation measurements on CTASI identify proximal LVO stroke patients with high sensitivity and specificity. This technique can aid in accurate and timely patient selection for thrombectomy, especially in primary stroke centers without CTP capacity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00062-020-00956-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-84635152021-10-08 Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography Reidler, Paul Stueckelschweiger, Lena Puhr-Westerheide, Daniel Feil, Katharina Kellert, Lars Dimitriadis, Konstantinos Tiedt, Steffen Herzberg, Moriz Rémi, Jan Liebig, Thomas Fabritius, Matthias P. Kunz, Wolfgang G. Clin Neuroradiol Original Article PURPOSE: Computed tomography angiography (CTA) is routinely used to detect large-vessel occlusion (LVO) in patients with suspected acute ischemic stroke; however, visual analysis is time consuming and prone to error. To evaluate solutions to support imaging triage, we tested performance of automated analysis of CTA source images (CTASI) at identifying patients with LVO. METHODS: Stroke patients with LVO were selected from a prospectively acquired cohort. A control group was selected from consecutive patients with clinically suspected stroke without signs of ischemia on CT perfusion (CTP) or infarct on follow-up. Software-based automated segmentation and Hounsfield unit (HU) measurements were performed on CTASI for all regions of the Alberta Stroke Program Early CT score (ASPECTS). We derived different parameters from raw measurements and analyzed their performance to identify patients with LVO using receiver operating characteristic curve analysis. RESULTS: The retrospective analysis included 145 patients, 79 patients with LVO stroke and 66 patients without stroke. The parameters hemispheric asymmetry ratio (AR), ratio between highest and lowest regional AR and M2-territory AR produced area under the curve (AUC) values from 0.95–0.97 (all p < 0.001) for detecting presence of LVO in the total population. Resulting sensitivity (sens)/specificity (spec) defined by the Youden index were 0.87/0.97–0.99. Maximum sens/spec defined by the specificity threshold ≥0.70 were 0.91–0.96/0.77–0.83. Performance in a small number of patients with isolated M2 occlusion was lower (AUC: 0.72–0.85). CONCLUSION: Automated attenuation measurements on CTASI identify proximal LVO stroke patients with high sensitivity and specificity. This technique can aid in accurate and timely patient selection for thrombectomy, especially in primary stroke centers without CTP capacity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00062-020-00956-5) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-09-16 2021 /pmc/articles/PMC8463515/ /pubmed/32939563 http://dx.doi.org/10.1007/s00062-020-00956-5 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Reidler, Paul
Stueckelschweiger, Lena
Puhr-Westerheide, Daniel
Feil, Katharina
Kellert, Lars
Dimitriadis, Konstantinos
Tiedt, Steffen
Herzberg, Moriz
Rémi, Jan
Liebig, Thomas
Fabritius, Matthias P.
Kunz, Wolfgang G.
Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography
title Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography
title_full Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography
title_fullStr Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography
title_full_unstemmed Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography
title_short Performance of Automated Attenuation Measurements at Identifying Large Vessel Occlusion Stroke on CT Angiography
title_sort performance of automated attenuation measurements at identifying large vessel occlusion stroke on ct angiography
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463515/
https://www.ncbi.nlm.nih.gov/pubmed/32939563
http://dx.doi.org/10.1007/s00062-020-00956-5
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