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TNM-O: ontology support for staging of malignant tumours

Overview of attention for article published in Journal of Biomedical Semantics, November 2016
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Title
TNM-O: ontology support for staging of malignant tumours
Published in
Journal of Biomedical Semantics, November 2016
DOI 10.1186/s13326-016-0106-9
Pubmed ID
Authors

Martin Boeker, Fábio França, Peter Bronsert, Stefan Schulz

Abstract

Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology by assigning TNM classes to real world pathology data. The TNM ontology uses the Foundational Model of Anatomy for anatomical entities and BioTopLite 2 as a domain top-level ontology. General rules for the TNM classification system and the specific TNM classification for colorectal tumours were axiomatised in description logic. Case-based information was collected from tumour documentation practice in the Comprehensive Cancer Centre of a large university hospital. Based on the ontology, a module was developed that classifies pathology data. TNM was represented as an information artefact, which consists of single representational units. Corresponding to every representational unit, tumours and tumour aggregates were defined. Tumour aggregates consist of the primary tumour and, if existing, of infiltrated regional lymph nodes and distant metastases. TNM codes depend on the location and certain qualities of the primary tumour (T), the infiltrated regional lymph nodes (N) and the existence of distant metastases (M). Tumour data from clinical and pathological documentation were successfully classified with the ontology. A first version of the TNM Ontology represents the TNM system for the description of the anatomical extent of malignant tumours. The present work demonstrates its representational power and completeness as well as its applicability for classification of instance data.

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Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 20%
Student > Master 8 15%
Researcher 7 13%
Student > Bachelor 5 9%
Professor 3 6%
Other 7 13%
Unknown 13 24%
Readers by discipline Count As %
Medicine and Dentistry 14 26%
Biochemistry, Genetics and Molecular Biology 11 20%
Computer Science 6 11%
Engineering 3 6%
Economics, Econometrics and Finance 1 2%
Other 1 2%
Unknown 18 33%