Introduction

The TNM classification has become the accepted basis of cancer staging [1]. The system has undergone several revisions, with the 7 th edition released in 2009 and the 8 th one in 2017. TNM supports treatment planning, prognosis, evaluation of treatment results, exchange of information between different participants in the treatment process as well as cancer research and control. The TNM coding is a "shorthand notation" with the following codes for the three main components, together with numeric modifiers to describe the extent of the disease [2]:

 T (tumour): primary tumour, codes: Tx, T0, Tis, T1-T4  N (node): metastatic regional lymph nodes, codes: NX, N0, N1-3  M (metastasis): distant metastasis, codes: M0 or M1 The meaning of the modifiers depends on the respective tumour entity. TX and NX: no assessment possible, T0, N0, M0: no evidence of tumour, Tis: carcinoma in-situ, numbers 1 -4: presence of tumour with increasing size or local extent. Depending on the type of tumour, further subdivisions are possible indicated by lower case characters (e.g. N2a and N2b). With a prefix, the pre-treatment cTNM (c = clinical) and postsurgical pTNM (p = pathological) classification are distinguished. A series of additional symbols exists, of which this work will only address the descriptors T, N, and M. TNM is different for each anatomical region, which yields more than sixty different sets of rules.

Table 1 describes the differences between the TNM versions for pancreas tumours. While there was just one set of rules for all pancreas tumours in TNM7, TMN8 distinguishes between tumours of the exocrine pancreas and well-differentiated tumours of the insulin-producing (neuroendocrine) pancreas (grades 1 and 2). The classification of neuroendocrine tumours of higher grades uses the rules for the exocrine pancreas. The table demonstrates that the classification of pancreas tumours is mainly based on size and extension into the adjacent tissue and that the view on the tumour charac-teristics and the rating of their contribution to the tumour malignancy has changed between TNM7 and TNM8. As a consequence, a tumour of the same extent can be coded very differently in these versions.

In the previous work on TNM for breast [4] and colon cancer [5], we proposed a description-logic [6] based representation of TNM and argued that its rooting in formal ontologies have advantages over its release as text, because axiomatic descriptions are more precise than textual ones. Thus, descriptions are formally decomposed into all their defining criteria. Further, an overarching TNM ontology can be used for automatic classification of clinical data [5].

In this paper we describe the next step towards a fully implemented TNM ontology, with the new feature of mapping between TNM versions. This allows us to re-classify tumours in different TNM versions.

Methods

Ontologies were created using Protégé 5.2 [7] in a modular approach. Organ and version-specific ontologies are imported into the "hub"-ontology TNM-O [5] under BioTopLite2 [8], [9]. The ontology TNM-O-BodyParts contains codes for anatomical entities with expressions borrowed from the Foundational Model of Anatomy FMA [10] whenever possible. All ontologies were imported into TNM-O. SWRL rules were set up in the human readable syntax as described in [11]. The ontologies were tested using the HermiT DL reasoner version 1.3.8 [12].

Results

Three pancreas ontologies were created (Pancreas TNM7, Exocrine Pancreas TNM8 and Neuroendocrine Pancreas TNM8) following a similar, albeit slightly improved structure as already described for breast cancer [4] and colorectal cancer [5]. The basic structure in each of these ontologies is the following: A tumour located in an anatomical region and with specific characteristics, e.g. defined by a quality and its value, is represented by a TNM code. A tumour can be a primary tumour or a tumour aggregate with metastatic regional lymph nodes and/or distant metastases. Classes for tumour qualities, value regions and the representational units were defined in the TNM-O "hub"-ontology and can thus be re-used to create ontologies representing TNM coding rules for various organs. TNM-O serves as the central ontology and imports all other OWL files, thus creating a modular structure as shown in Figure 1. All classes for anatomy were defined in the ontology TNM-O-Bodyparts, using expressions from FMA [10] whenever possible. A bridge ontology contains mappings between the TNM versions.

Mapping rules between TNM7 and TNM8 or vice versa were defined as SWRL rules for every possible combination of conditions for TNM rules. The mapping rules follow the general structure:

TNM7 tumour ∧ additional criteria ⇒ TNM8 tumour An example for such a rule in human readable syntax is listed below. This shows the rules for the re-classification of a tumour class from the TNM7 ontology, "InvasivePancreasTumorNotBeyondCeliacTrunkOrSuperiorMesentericArtery" which is represented by TNM7 code T3 (compare table 1). As TNM8 does not describe a tumour with exactly the same conditions, this tumour can only be transformed into a tumour class in one of the pancreas TNM8 ontologies by providing further information. In the example below the tumour invades the Common Hepatic Artery and thus can be transformed into the TNM8 tumour class "InvasiveExocrinePancreasTumor-InfiltratingDefinedBloodVessels" represented by TNM8 code T4 (compare table 1).

InvasivePancreasTumorNotBeyondCeliacTrunkOrSuperiorMesentericArtery (?x)

∧ isIncludedIn(?x,?exopancreas) ∧ ExocrinePancreas(?exopancreas) ∧ hasPart (?x,?tumorpart) ∧ isIncludedIn (?tumorpart,?loc) ∧ CommonHepaticArtery(?loc) ⇒ InvasiveExocrinePancreasTumorInfiltratingdefinedBloodVessels (?x)

This mapping approach can be used to re-classify a tumour already classified in one TNM version, if the additional criteria needed for the coding in the other TNM version are known. In addition, the approach has the advantage that all "mapping rules" can be easily listed using the SWRL tab in Protégé. The SWRL rules were defined for all possible combinations of tumour characteristics defined in TNM7 and TNM8 and tested with individuals representing all these cases. Another approach without SWRL rules, implementing the mapping rules using a bridge ontology with additional subclass relations or other axioms, is also under evaluation.

Conclusion

A modular approach was used to create a set of ontologies for the representation of the TNM coding rules across TNM versions. It could be demonstrated that mapping between different versions of the TNM scheme can be implemented using a "bridge" ontology with SWRL rules. This could be a useful tool for the re-assignment of TNMcodes in different TNM versions and can easily be extended to represent other organs as well. The ontology will be made available as open source via GitHub.