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Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography
OBJECTIVES: Outcome of endovascular treatment in acute ischemic stroke patients depends on collateral circulation to provide blood supply to the ischemic territory. We evaluated the performance of a commercially available algorithm for assessing the collateral score (CS) in acute ischemic stroke pat...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279191/ https://www.ncbi.nlm.nih.gov/pubmed/35244761 http://dx.doi.org/10.1007/s00330-022-08627-4 |
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author | Wolff, Lennard Uniken Venema, Simone M. Luijten, Sven P. R. Hofmeijer, Jeannette Martens, Jasper M. Bernsen, Marie Louise E. van Es, Adriaan C. G. M. van Doormaal, Pieter Jan Dippel, Diederik W. J. van Zwam, Wim van Walsum, Theo van der Lugt, Aad |
author_facet | Wolff, Lennard Uniken Venema, Simone M. Luijten, Sven P. R. Hofmeijer, Jeannette Martens, Jasper M. Bernsen, Marie Louise E. van Es, Adriaan C. G. M. van Doormaal, Pieter Jan Dippel, Diederik W. J. van Zwam, Wim van Walsum, Theo van der Lugt, Aad |
author_sort | Wolff, Lennard |
collection | PubMed |
description | OBJECTIVES: Outcome of endovascular treatment in acute ischemic stroke patients depends on collateral circulation to provide blood supply to the ischemic territory. We evaluated the performance of a commercially available algorithm for assessing the collateral score (CS) in acute ischemic stroke patients. METHODS: Retrospectively, baseline CTA scans (≤ 3-mm slice thickness) with an intracranial carotid artery (ICA), middle cerebral artery segment M1 or M2 occlusion, from the MR CLEAN Registry (n = 1627) were evaluated. All CTA scans were evaluated for visual CS (0–3) by eight expert radiologists (reference standard). A Web-based AI algorithm quantified the collateral circulation (0–100%) for correctly detected occlusion sides. Agreement between visual CS and categorized automated CS (0: 0%, 1: > 0– ≤ 50%, 2: > 50– < 100%, 3: 100%) was assessed. Area under the curve (AUC) values for classifying patients in having good (CS: 2–3) versus poor (CS: 0–1) collaterals and for predicting functional independence (90-day modified Rankin Scale 0–2) were computed. Influence of CTA acquisition timing after contrast material administration was reported. RESULTS: In the analyzed scans (n = 1024), 59% agreement was found between visual CS and automated CS. An AUC of 0.87 (95% CI: 0.85–0.90) was found for discriminating good versus poor CS. Timing of CTA acquisition did not influence discriminatory performance. AUC for predicting functional independence was 0.66 (95% CI 0.62–0.69) for automated CS, similar to visual CS 0.64 (95% CI 0.61–0.68). CONCLUSIONS: The automated CS performs similar to radiologists in determining a good versus poor collateral score and predicting functional independence in acute ischemic stroke patients with a large vessel occlusion. KEY POINTS: • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in determining a good versus poor collateral score. • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in predicting functional independence in acute ischemic stroke patients with a large vessel occlusion. • The timing of computed tomography angiography acquisition after contrast material administration did not influence the performance of automated quantification of the collateral status. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08627-4. |
format | Online Article Text |
id | pubmed-9279191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-92791912022-07-15 Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography Wolff, Lennard Uniken Venema, Simone M. Luijten, Sven P. R. Hofmeijer, Jeannette Martens, Jasper M. Bernsen, Marie Louise E. van Es, Adriaan C. G. M. van Doormaal, Pieter Jan Dippel, Diederik W. J. van Zwam, Wim van Walsum, Theo van der Lugt, Aad Eur Radiol Imaging Informatics and Artificial Intelligence OBJECTIVES: Outcome of endovascular treatment in acute ischemic stroke patients depends on collateral circulation to provide blood supply to the ischemic territory. We evaluated the performance of a commercially available algorithm for assessing the collateral score (CS) in acute ischemic stroke patients. METHODS: Retrospectively, baseline CTA scans (≤ 3-mm slice thickness) with an intracranial carotid artery (ICA), middle cerebral artery segment M1 or M2 occlusion, from the MR CLEAN Registry (n = 1627) were evaluated. All CTA scans were evaluated for visual CS (0–3) by eight expert radiologists (reference standard). A Web-based AI algorithm quantified the collateral circulation (0–100%) for correctly detected occlusion sides. Agreement between visual CS and categorized automated CS (0: 0%, 1: > 0– ≤ 50%, 2: > 50– < 100%, 3: 100%) was assessed. Area under the curve (AUC) values for classifying patients in having good (CS: 2–3) versus poor (CS: 0–1) collaterals and for predicting functional independence (90-day modified Rankin Scale 0–2) were computed. Influence of CTA acquisition timing after contrast material administration was reported. RESULTS: In the analyzed scans (n = 1024), 59% agreement was found between visual CS and automated CS. An AUC of 0.87 (95% CI: 0.85–0.90) was found for discriminating good versus poor CS. Timing of CTA acquisition did not influence discriminatory performance. AUC for predicting functional independence was 0.66 (95% CI 0.62–0.69) for automated CS, similar to visual CS 0.64 (95% CI 0.61–0.68). CONCLUSIONS: The automated CS performs similar to radiologists in determining a good versus poor collateral score and predicting functional independence in acute ischemic stroke patients with a large vessel occlusion. KEY POINTS: • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in determining a good versus poor collateral score. • Software for automated quantification of intracerebral collateral circulation on computed tomography angiography performs similar to expert radiologists in predicting functional independence in acute ischemic stroke patients with a large vessel occlusion. • The timing of computed tomography angiography acquisition after contrast material administration did not influence the performance of automated quantification of the collateral status. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-08627-4. Springer Berlin Heidelberg 2022-03-04 2022 /pmc/articles/PMC9279191/ /pubmed/35244761 http://dx.doi.org/10.1007/s00330-022-08627-4 Text en © The Author(s) 2022 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 | Imaging Informatics and Artificial Intelligence Wolff, Lennard Uniken Venema, Simone M. Luijten, Sven P. R. Hofmeijer, Jeannette Martens, Jasper M. Bernsen, Marie Louise E. van Es, Adriaan C. G. M. van Doormaal, Pieter Jan Dippel, Diederik W. J. van Zwam, Wim van Walsum, Theo van der Lugt, Aad Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
title | Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
title_full | Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
title_fullStr | Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
title_full_unstemmed | Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
title_short | Diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
title_sort | diagnostic performance of an algorithm for automated collateral scoring on computed tomography angiography |
topic | Imaging Informatics and Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9279191/ https://www.ncbi.nlm.nih.gov/pubmed/35244761 http://dx.doi.org/10.1007/s00330-022-08627-4 |
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