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Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease

BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliabl...

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Autores principales: Leung, Lester Y., Fu, Sunyang, Luetmer, Patrick H., Kallmes, David F., Madan, Neel, Weinstein, Gene, Lehman, Vance T., Rydberg, Charlotte H., Nelson, Jason, Liu, Hongfang, Kent, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8111708/
https://www.ncbi.nlm.nih.gov/pubmed/33975556
http://dx.doi.org/10.1186/s12883-021-02221-9
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author Leung, Lester Y.
Fu, Sunyang
Luetmer, Patrick H.
Kallmes, David F.
Madan, Neel
Weinstein, Gene
Lehman, Vance T.
Rydberg, Charlotte H.
Nelson, Jason
Liu, Hongfang
Kent, David M.
author_facet Leung, Lester Y.
Fu, Sunyang
Luetmer, Patrick H.
Kallmes, David F.
Madan, Neel
Weinstein, Gene
Lehman, Vance T.
Rydberg, Charlotte H.
Nelson, Jason
Liu, Hongfang
Kent, David M.
author_sort Leung, Lester Y.
collection PubMed
description BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. METHODS: Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. RESULTS: For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46–0.69) and WMD (k = 0.49, 95 % CI 0.35–0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53–0.76) and moderate (k = 0.52, 95 % CI 0.39–0.65) for the presence of WMD. CONCLUSIONS: Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-021-02221-9.
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spelling pubmed-81117082021-05-11 Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease Leung, Lester Y. Fu, Sunyang Luetmer, Patrick H. Kallmes, David F. Madan, Neel Weinstein, Gene Lehman, Vance T. Rydberg, Charlotte H. Nelson, Jason Liu, Hongfang Kent, David M. BMC Neurol Research BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. METHODS: Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. RESULTS: For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46–0.69) and WMD (k = 0.49, 95 % CI 0.35–0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53–0.76) and moderate (k = 0.52, 95 % CI 0.39–0.65) for the presence of WMD. CONCLUSIONS: Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12883-021-02221-9. BioMed Central 2021-05-11 /pmc/articles/PMC8111708/ /pubmed/33975556 http://dx.doi.org/10.1186/s12883-021-02221-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Leung, Lester Y.
Fu, Sunyang
Luetmer, Patrick H.
Kallmes, David F.
Madan, Neel
Weinstein, Gene
Lehman, Vance T.
Rydberg, Charlotte H.
Nelson, Jason
Liu, Hongfang
Kent, David M.
Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
title Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
title_full Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
title_fullStr Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
title_full_unstemmed Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
title_short Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
title_sort agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8111708/
https://www.ncbi.nlm.nih.gov/pubmed/33975556
http://dx.doi.org/10.1186/s12883-021-02221-9
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