Integration of Imaging Signs into RadLex

Shore, Matthew W.; Rubin, Daniel L.; Kahn, Charles Η.

doi:10.1007/s10278-011-9386-x

Cited by 22 publications

(13 citation statements)

References 15 publications

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“…To maintain their utility as "best practices" for the radiology community, both RadLex® and RSNA reporting templates should continue to grow (Shore, Rubin, and Kahn, 2012). The use of standardized biomedical terminologies in reporting templates can reduce communication errors.The unmatched terms (the terms occurring in the free-text reports but not in the templates) from the reporting templates provide a "bottom-up" inventory of entities used in radiology reports, which most frequently occurred ones might be served as candidate terms for inclusion in the templates and therefore enhancestandardized terminology coverage of the templates.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Terminology Coverage in Radiology Reporting Templates and Free-text Reports

Hong¹,

Zhang²

2015

International Journal of Knowledge Content Development & Te

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The Radiological Society of North America (RSNA) is improving reporting practices by developing an online library of clear and consistent report templates. To compare term occurrences in free-text radiology reports and RSNA reporting templates, the Wilcoxon signed-rank test method was applied to investigate how much of the content of conventional narrative reports is covered by the terms included in the RSNA reporting templates. The results show that the RSNA reporting templates cover most terms that appear in actual radiology reports. The Wilcoxon test may be helpful in evaluatingexisting templates and guiding the enhancement of reporting templates.

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Section: Discussionmentioning

confidence: 99%

Investigation of Terminology Coverage in Radiology Reporting Templates and Free-text Reports

Hong¹,

Zhang²

2015

International Journal of Knowledge Content Development & Te

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“…Biomarker Standardization Initiative (IBSI) (20), which have boosted the standard imaging feature-set construction. Furthermore, RadLex, a new radiology lexicon resource with more than 40 000 standardized terms covering all aspects of radiology, has significantly advanced communication in radiology (21)(22)(23). In addition, other studies have also made recommendations for reducing imaging feature redundancy (13,17).…”

Section: Categories Of Extraction Criteriamentioning

confidence: 99%

Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies

Wang

Liu

Xie

et al. 2020

Radiology: Imaging Cancer

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To summarize the data of previously reported medical imaging features on human malignancies to provide a scientific basis for more credible imaging feature selection for future studies. Materials and Methods: A search was performed in PubMed from database inception through March 23, 2018, for studies clearly stating the decoding of medical imaging features for malignancy-related objectives and/or hypotheses. The Newcastle-Ottawa scale was used for quality assessment of the included studies. Unsupervised hierarchical clustering was performed on the manually extracted features from each included study to identify the application rules of medical imaging features across human malignancies. CT images of 1000 retrospective patients with non-small cell lung cancer were used to reveal a pattern for the value distribution of complex texture features. Results: A total of 5026 imaging features of malignancies affecting 20 parts of the human body from 930 original articles were collated and assessed in this study. A meta-feature construct was proposed to facilitate the investigation of details of any high-dimensional complex imaging features of malignancy. A correlation atlas was constructed to clarify the general rules of applying medical imaging features to the analysis of human malignancy. Assessment of this data revealed a pattern of value distributions of the most commonly reported texture features across human malignancies. Furthermore, the significant expression of the gene mutational signature 1B across human cancer was highly consistent with the presence of the run length imaging feature across different human malignancy types. Conclusion: The results of this study may facilitate more credible imaging feature selection in all oncology tasks across a wide spectrum of human malignancies and help to reduce bias and redundancies in future medical imaging studies. Supplemental material is available for this article.

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“…RadLex is an ontology of radiology terms developed and published by the Radiological Society of North America (RSNA) [6][7][8]. It was developed to address gaps in general clinical vocabularies [9], and incorporates terms for anatomy, diseases, and imaging signs [10]. RadLex has been used to index radiological literature, learning materials, and radiological procedures.…”

Section: Introductionmentioning

confidence: 99%

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

Filice

Kahn

2019

J Digit Imaging

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An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal (Bmay cause^) relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining.

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Integration of Imaging Signs into RadLex

Cited by 22 publications

References 15 publications

Investigation of Terminology Coverage in Radiology Reporting Templates and Free-text Reports

Investigation of Terminology Coverage in Radiology Reporting Templates and Free-text Reports

Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

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