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Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling
Objectives: To investigate the relationship between imaging features derived from lesion loads and 3 month clinical assessments in ischemic stroke patients. To support clinically implementable predictive modeling with information from lesion-load features. Methods: A retrospective cohort of ischemic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141854/ https://www.ncbi.nlm.nih.gov/pubmed/30254601 http://dx.doi.org/10.3389/fneur.2018.00737 |
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author | Habegger, Simon Wiest, Roland Weder, Bruno J. Mordasini, Pasquale Gralla, Jan Häni, Levin Jung, Simon Reyes, Mauricio McKinley, Richard |
author_facet | Habegger, Simon Wiest, Roland Weder, Bruno J. Mordasini, Pasquale Gralla, Jan Häni, Levin Jung, Simon Reyes, Mauricio McKinley, Richard |
author_sort | Habegger, Simon |
collection | PubMed |
description | Objectives: To investigate the relationship between imaging features derived from lesion loads and 3 month clinical assessments in ischemic stroke patients. To support clinically implementable predictive modeling with information from lesion-load features. Methods: A retrospective cohort of ischemic stroke patients was studied. The dataset was dichotomized based on revascularization treatment outcome (TICI score). Three lesion delineations were derived from magnetic resonance imaging in each group: two clinically implementable (threshold based and fully automatic prediction) and 90-day follow-up as final groundtruth. Lesion load imaging features were created through overlay of the lesion delineations on a histological brain atlas, and were correlated with the clinical assessment (NIHSS). Significance of the correlations was assessed by constructing confidence intervals using bootstrap sampling. Results: Overall, high correlations between lesion loads and clinical score were observed (up to 0.859). Delineations derived from acute imaging yielded on average somewhat lower correlations than delineations derived from 90-day follow-up imaging. Correlations suggest that both total lesion volume and corticospinal tract lesion load are associated with functional outcome, and in addition highlight other potential areas associated with poor clinical outcome, including the primary somatosensory cortex BA3a. Fully automatic prediction was comparable to ADC threshold-based delineation on the successfully treated cohort and superior to the Tmax threshold-based delineation in the unsuccessfully treated cohort. Conclusions: The confirmation of established predictors for stroke outcome (e.g., corticospinal tract integrity and total lesion volume) gives support to the proposed methodology—relating acute lesion loads to 3 month outcome assessments by way of correlation. Furthermore, the preliminary results indicate an association of further brain regions and structures with three month NIHSS outcome assessments. Hence, prediction models might observe an increased accuracy when incorporating regional (instead of global) lesion loads. Also, the results lend support to the clinical utilization of the automatically predicted volumes from FASTER, rather than the simpler DWI and PWI lesion delineations. |
format | Online Article Text |
id | pubmed-6141854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61418542018-09-25 Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling Habegger, Simon Wiest, Roland Weder, Bruno J. Mordasini, Pasquale Gralla, Jan Häni, Levin Jung, Simon Reyes, Mauricio McKinley, Richard Front Neurol Neurology Objectives: To investigate the relationship between imaging features derived from lesion loads and 3 month clinical assessments in ischemic stroke patients. To support clinically implementable predictive modeling with information from lesion-load features. Methods: A retrospective cohort of ischemic stroke patients was studied. The dataset was dichotomized based on revascularization treatment outcome (TICI score). Three lesion delineations were derived from magnetic resonance imaging in each group: two clinically implementable (threshold based and fully automatic prediction) and 90-day follow-up as final groundtruth. Lesion load imaging features were created through overlay of the lesion delineations on a histological brain atlas, and were correlated with the clinical assessment (NIHSS). Significance of the correlations was assessed by constructing confidence intervals using bootstrap sampling. Results: Overall, high correlations between lesion loads and clinical score were observed (up to 0.859). Delineations derived from acute imaging yielded on average somewhat lower correlations than delineations derived from 90-day follow-up imaging. Correlations suggest that both total lesion volume and corticospinal tract lesion load are associated with functional outcome, and in addition highlight other potential areas associated with poor clinical outcome, including the primary somatosensory cortex BA3a. Fully automatic prediction was comparable to ADC threshold-based delineation on the successfully treated cohort and superior to the Tmax threshold-based delineation in the unsuccessfully treated cohort. Conclusions: The confirmation of established predictors for stroke outcome (e.g., corticospinal tract integrity and total lesion volume) gives support to the proposed methodology—relating acute lesion loads to 3 month outcome assessments by way of correlation. Furthermore, the preliminary results indicate an association of further brain regions and structures with three month NIHSS outcome assessments. Hence, prediction models might observe an increased accuracy when incorporating regional (instead of global) lesion loads. Also, the results lend support to the clinical utilization of the automatically predicted volumes from FASTER, rather than the simpler DWI and PWI lesion delineations. Frontiers Media S.A. 2018-09-11 /pmc/articles/PMC6141854/ /pubmed/30254601 http://dx.doi.org/10.3389/fneur.2018.00737 Text en Copyright © 2018 Habegger, Wiest, Weder, Mordasini, Gralla, Häni, Jung, Reyes and McKinley. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Habegger, Simon Wiest, Roland Weder, Bruno J. Mordasini, Pasquale Gralla, Jan Häni, Levin Jung, Simon Reyes, Mauricio McKinley, Richard Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling |
title | Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling |
title_full | Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling |
title_fullStr | Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling |
title_full_unstemmed | Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling |
title_short | Relating Acute Lesion Loads to Chronic Outcome in Ischemic Stroke–An Exploratory Comparison of Mismatch Patterns and Predictive Modeling |
title_sort | relating acute lesion loads to chronic outcome in ischemic stroke–an exploratory comparison of mismatch patterns and predictive modeling |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6141854/ https://www.ncbi.nlm.nih.gov/pubmed/30254601 http://dx.doi.org/10.3389/fneur.2018.00737 |
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