TNM-O: A modular ontological approach for the representation of tumour entities across TNM versions

Susanne Zabka, Stefan Schulz, Oliver Brunner, Martin Boeker

Research output: Contribution to journalConference articlepeer-review


The TNM classification (Tumour-Node-Metastasis) is the most impor-tant coding scheme used to stage tumours based on size or location. Its coding rules often change with different TNM versions, such that the same tumour may be represented by different codes in different TNM versions. We present an ontology-based modular architecture for the management of the TNM coding system. Separate OWL files representing the coding rules for pancreas tumours in the considerably different TNM versions 7 and 8 were created to demonstrate how mappings between TNM versions can be supported. A modular approach with BioTopLite2 as domain top-level ontology, a "hub"-ontology TNM-O containing general TNM and tumour criteria and an ontology for the anatomical entities based on the Foundational Model of Anatomy (FMA) was used as a common basis. For each tumour location and TNM version additional OWL files are created, following strictly defined design patterns. An important feature of the architecture is that for each tumour location and TNM version mappings are encoded in bridging ontologies, which enable re-classification of tumour instances. This work describes a bridging approach using SWRL rules to represent the mapping criteria between the TNM versions, which were tested with instance data. We could show that a tumour with defined characteristics was correctly classified in different versions of the TNM classification.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2017
Externally publishedYes
Event2017 Joint Ontology Workshops Episode 3: The Tyrolean Autumn of Ontology, JOWO 2017 - Bozen-Bolzano, Italy
Duration: 21 Sep 201723 Sep 2017


  • Ontology
  • Pancreas tumour
  • SWRL rules
  • TNM classification
  • TNM-O


Dive into the research topics of 'TNM-O: A modular ontological approach for the representation of tumour entities across TNM versions'. Together they form a unique fingerprint.

Cite this