RadLex – deutsche Version: ein radiologisches Lexikon zur Indexierung von Bild- und Befunddaten

Marwede, Dirk; Daumke, Philipp; Markó, Kornél G.; Lobsien, D; Schulz, Stefan; Kahn, T

doi:10.1055/s-2008-1027895

Cited by 17 publications

(5 citation statements)

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“…It includes functions such as negation recognition, morphosemantic analysis, hierarchical terminology and key word weighting. A radiological term mapping component that utilizes the German translation of RADLEX [8,9] was first supplemented by the relevant terms from ICD-10. The included terms (RADLEX + ICD-10) were then assigned to the risk groups.…”

Section: Information Extraction Approachesmentioning

confidence: 99%

Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes

et al. 2019

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Background In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. Methods From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings reports were assigned to the given findings groups using machine learning methods. To assess the reliability and validity of the automated process, the automated and two independent manual mappings were compared for matches in multiple runs. Results Manual classification was too time-consuming. In the case of automated keywording, the classification according to ICD-10 turned out to be unsuitable for our data. It also showed that the keyword search does not deliver reliable results. Computer-aided text mining and machine learning resulted in reliable results. The inter-rater reliability of the two manual classifications, as well as the machine and manual classification was very high. Both manual classifications were consistent in 93% of all findings. The kappa coefficient is 0.89 [95% confidence interval (CI) 0.87–0.92]. The automatic classification agreed with the independent, second manual classification in 86% of all findings (Kappa coefficient 0.79 [95% CI 0.75–0.81]). Discussion The classification of the software AEP was very good. In our study, however, it followed a systematic pattern. Most misclassifications were found in findings that indicate an increased risk of cancer. The free-text structure of the findings raises concerns about the feasibility of a purely automated analysis. The combination of human intellect and intelligent, adaptive software appears most suitable for mining unstructured but important textual information for research.

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Section: Information Extraction Approachesmentioning

confidence: 99%

Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes

et al. 2019

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“…On the one hand, it is not possible to visualize the bloodbrain interface directly via conventional MRI-what you might see is a T1 enhancement due to extravasation of contrast medium into the extracellular space as a result of a disruption of this neuroprotective barrier, but not the barrier itself [27,28] and therefore it could be argued that this concept may be expendable. On the other hand, a primary range of the RadLex application involves indexing of large databases of radiological free-text reports for educational and research purposes, which necessarily requires the highest achievable degree of completeness [3,6]. Hence the issue of implementation of a specific term into RadLex should be decided upon preferably by the fact how deeply ingrained this concept is in our radiological everyday communication.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, it provides extensive background information on their specific relationships [3,5]. A few years after the release of the first version of this radiological terminology in English language in 2005, a translation into German language was published that already comprised of more than 6,000 items [6]. Since then, both the English and German editions of the RadLex have continuously been updated and augmented and the current version 4.0 of this radiology domain lexicon already contained more than 46,000 entities at the time of publication in January, 2019 [7].…”

Section: Introductionmentioning

confidence: 99%

Determining the applicability of the RSNA radiology lexicon (RadLex) in high-grade glioma MRI reporting—a preliminary study on 20 consecutive cases with newly diagnosed glioblastoma

et al. 2022

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Background The implementation of a collective terminology in radiological reporting such as the RSNA radiological lexicon (RadLex) yields many benefits including unambiguous communication of findings, improved education, and fostering data mining for research purposes. While some fields in general radiology have already been evaluated so far, this is the first exploratory approach to assess the applicability of the RadLex terminology to glioblastoma (GBM) MRI reporting. Methods Preoperative brain MRI reports of 20 consecutive patients with newly diagnosed GBM (mean age 68.4 ± 10.8 years; 12 males) between January and October 2010 were retrospectively identified. All terms related to the tumor as well as their frequencies of mention were extracted from the MRI reports by two independent neuroradiologists. Every item was subsequently analyzed with respect to an equivalent RadLex representation and classified into one of four groups as follows: 1. verbatim RadLex entity, 2. synonymous/multiple equivalent(s), 3. combination of RadLex concepts, or 4. no RadLex equivalent. Additionally, verbatim entities were categorized using the hierarchical RadLex Tree Browser. Results A total of 160 radiological terms were gathered. 123/160 (76.9%) items showed literal RadLex equivalents, 9/160 (5.6%) items had synonymous (non-verbatim) or multiple counterparts, 21/160 (13.1%) items were represented by means of a combination of concepts, and 7/160 (4.4%) entities could not eventually be transferred adequately into the RadLex ontology. Conclusions Our results suggest a sufficient term coverage of the RadLex terminology for GBM MRI reporting. If applied extensively, it may improve communication of radiological findings and facilitate data mining for large-scale research purposes.

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“…DICOM defines images, and HL7 defines documents [10,11]. ICD10 defines clinical situations and diseases, and Radlex® defines radiological vocabulary as a tool by which to index radiological teaching files [12,13].…”

Section: Appc and Other Standardsmentioning

confidence: 99%

APPC—A new standardised coding system for trans-organisational PACS retrieval

Fruehwald¹,

Lindner²,

Mostbeck

et al. 2010

Eur Radiol

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RadLex – deutsche Version: ein radiologisches Lexikon zur Indexierung von Bild- und Befunddaten

Cited by 17 publications

References 0 publications

Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes

Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes

Determining the applicability of the RSNA radiology lexicon (RadLex) in high-grade glioma MRI reporting—a preliminary study on 20 consecutive cases with newly diagnosed glioblastoma

APPC—A new standardised coding system for trans-organisational PACS retrieval

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