Cargando…
Semantic representation of reported measurements in radiology
BACKGROUND: In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722630/ https://www.ncbi.nlm.nih.gov/pubmed/26801764 http://dx.doi.org/10.1186/s12911-016-0248-9 |
_version_ | 1782411390089166848 |
---|---|
author | Oberkampf, Heiner Zillner, Sonja Overton, James A. Bauer, Bernhard Cavallaro, Alexander Uder, Michael Hammon, Matthias |
author_facet | Oberkampf, Heiner Zillner, Sonja Overton, James A. Bauer, Bernhard Cavallaro, Alexander Uder, Michael Hammon, Matthias |
author_sort | Oberkampf, Heiner |
collection | PubMed |
description | BACKGROUND: In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. METHODS: We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. RESULTS: The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. CONCLUSIONS: The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements. |
format | Online Article Text |
id | pubmed-4722630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47226302016-01-23 Semantic representation of reported measurements in radiology Oberkampf, Heiner Zillner, Sonja Overton, James A. Bauer, Bernhard Cavallaro, Alexander Uder, Michael Hammon, Matthias BMC Med Inform Decis Mak Research Article BACKGROUND: In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. METHODS: We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. RESULTS: The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. CONCLUSIONS: The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements. BioMed Central 2016-01-22 /pmc/articles/PMC4722630/ /pubmed/26801764 http://dx.doi.org/10.1186/s12911-016-0248-9 Text en © Oberkampf et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Oberkampf, Heiner Zillner, Sonja Overton, James A. Bauer, Bernhard Cavallaro, Alexander Uder, Michael Hammon, Matthias Semantic representation of reported measurements in radiology |
title | Semantic representation of reported measurements in radiology |
title_full | Semantic representation of reported measurements in radiology |
title_fullStr | Semantic representation of reported measurements in radiology |
title_full_unstemmed | Semantic representation of reported measurements in radiology |
title_short | Semantic representation of reported measurements in radiology |
title_sort | semantic representation of reported measurements in radiology |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722630/ https://www.ncbi.nlm.nih.gov/pubmed/26801764 http://dx.doi.org/10.1186/s12911-016-0248-9 |
work_keys_str_mv | AT oberkampfheiner semanticrepresentationofreportedmeasurementsinradiology AT zillnersonja semanticrepresentationofreportedmeasurementsinradiology AT overtonjamesa semanticrepresentationofreportedmeasurementsinradiology AT bauerbernhard semanticrepresentationofreportedmeasurementsinradiology AT cavallaroalexander semanticrepresentationofreportedmeasurementsinradiology AT udermichael semanticrepresentationofreportedmeasurementsinradiology AT hammonmatthias semanticrepresentationofreportedmeasurementsinradiology |