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T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting

BACKGROUND: In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data...

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Autores principales: Nobel, J. Martijn, Puts, Sander, Weiss, Jakob, Aerts, Hugo J. W. L., Mak, Raymond H., Robben, Simon G. F., Dekker, André L. A. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192634/
https://www.ncbi.nlm.nih.gov/pubmed/34114076
http://dx.doi.org/10.1186/s13244-021-01018-1
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author Nobel, J. Martijn
Puts, Sander
Weiss, Jakob
Aerts, Hugo J. W. L.
Mak, Raymond H.
Robben, Simon G. F.
Dekker, André L. A. J.
author_facet Nobel, J. Martijn
Puts, Sander
Weiss, Jakob
Aerts, Hugo J. W. L.
Mak, Raymond H.
Robben, Simon G. F.
Dekker, André L. A. J.
author_sort Nobel, J. Martijn
collection PubMed
description BACKGROUND: In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification. This structuring tool was translated and validated on English radiological free-text reports. A rule-based algorithm to classify T-stage was trained and validated on, respectively, 200 and 225 English free-text radiological reports from diagnostic computed tomography (CT) obtained for staging of patients with lung cancer. The automated T-stage extracted by the algorithm from the report was compared to manual staging. A graphical user interface was built for training purposes to visualize the results of the algorithm by highlighting the extracted concepts and its modifying context. RESULTS: Accuracy of the T-stage classifier was 0.89 in the validation set, 0.84 when considering the T-substages, and 0.76 when only considering tumor size. Results were comparable with the Dutch results (respectively, 0.88, 0.89 and 0.79). Most errors were made due to ambiguity issues that could not be solved by the rule-based nature of the algorithm. CONCLUSIONS: NLP can be successfully applied for staging lung cancer from free-text radiological reports in different languages. Focused introduction of machine learning should be introduced in a hybrid approach to improve performance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-021-01018-1.
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spelling pubmed-81926342021-06-28 T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting Nobel, J. Martijn Puts, Sander Weiss, Jakob Aerts, Hugo J. W. L. Mak, Raymond H. Robben, Simon G. F. Dekker, André L. A. J. Insights Imaging Original Article BACKGROUND: In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification. This structuring tool was translated and validated on English radiological free-text reports. A rule-based algorithm to classify T-stage was trained and validated on, respectively, 200 and 225 English free-text radiological reports from diagnostic computed tomography (CT) obtained for staging of patients with lung cancer. The automated T-stage extracted by the algorithm from the report was compared to manual staging. A graphical user interface was built for training purposes to visualize the results of the algorithm by highlighting the extracted concepts and its modifying context. RESULTS: Accuracy of the T-stage classifier was 0.89 in the validation set, 0.84 when considering the T-substages, and 0.76 when only considering tumor size. Results were comparable with the Dutch results (respectively, 0.88, 0.89 and 0.79). Most errors were made due to ambiguity issues that could not be solved by the rule-based nature of the algorithm. CONCLUSIONS: NLP can be successfully applied for staging lung cancer from free-text radiological reports in different languages. Focused introduction of machine learning should be introduced in a hybrid approach to improve performance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-021-01018-1. Springer International Publishing 2021-06-10 /pmc/articles/PMC8192634/ /pubmed/34114076 http://dx.doi.org/10.1186/s13244-021-01018-1 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/) .
spellingShingle Original Article
Nobel, J. Martijn
Puts, Sander
Weiss, Jakob
Aerts, Hugo J. W. L.
Mak, Raymond H.
Robben, Simon G. F.
Dekker, André L. A. J.
T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
title T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
title_full T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
title_fullStr T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
title_full_unstemmed T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
title_short T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
title_sort t-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192634/
https://www.ncbi.nlm.nih.gov/pubmed/34114076
http://dx.doi.org/10.1186/s13244-021-01018-1
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