Semantic representation of reported measurements in radiology

dc.contributor.authorOberkampf, Heiner
dc.contributor.authorZillner, Sonja
dc.contributor.authorOverton, James A.
dc.contributor.authorBauer, Bernhard
dc.contributor.authorCavallaro, Alexander
dc.contributor.authorUder, Michael
dc.contributor.authorHammon, Matthias
dc.date.accessioned2016-12-30
dc.date.available2016-12-28
dc.date.created2016
dc.date.issued2016-12-30
dc.description.abstractBackground 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.en
dc.identifier.citationBMC Medical Informatics and Decision Making 16 (2016). <http://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0248-9>
dc.identifier.doihttps://doi.org/10.1186/s12911-016-0248-9
dc.identifier.opus-id8022
dc.identifier.urihttps://open.fau.de/handle/openfau/8022
dc.identifier.urnurn:nbn:de:bvb:29-opus4-80221
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/de/deed.de
dc.subjectRadiology
dc.subjectMeasurement
dc.subjectClassification
dc.subjectOntology
dc.subjectOBO
dc.subjectOpen Biological and Biomedical Ontologies
dc.subjectRECIST
dc.subjectFollow-up
dc.subject.ddcDDC Classification::6 Technik, Medizin, angewandte Wissenschaften :: 61 Medizin und Gesundheit :: 610 Medizin und Gesundheit
dc.titleSemantic representation of reported measurements in radiologyen
dc.typearticle
dcterms.publisherFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
local.journal.titleBMC Medical Informatics and Decision Making
local.journal.volume16
local.sendToDnbfree*
local.subject.fakultaetMedizinische Fakultät
local.subject.gnd-
local.subject.sammlungUniversität Erlangen-Nürnberg / Open Access Artikel ohne Förderung / Open Access Artikel ohne Förderung 2016
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
8022_Oberkampf_semantic.pdf
Size:
2.77 MB
Format:
Adobe Portable Document Format
Description: