=Paper=
{{Paper
|id=Vol-2050/odls-paper3
|storemode=property
|title=TNM-O: A Modular Ontological Approach for the
Representation of Tumour Entities across TNM Versions
|pdfUrl=https://ceur-ws.org/Vol-2050/ODLS_paper_3.pdf
|volume=Vol-2050
|authors=Susanne Zabka,Stefan Schulz,Oliver Brunner,Martin Boeker
|dblpUrl=https://dblp.org/rec/conf/jowo/ZabkaSBB17
}}
==TNM-O: A Modular Ontological Approach for the
Representation of Tumour Entities across TNM Versions==
https://ceur-ws.org/Vol-2050/ODLS_paper_3.pdf
TNM-O: A Modular Ontological Approach
for the Representation of Tumour Entities
across TNM Versions
Susanne Zabkaa,d, Stefan Schulzb, Oliver Brunnerc,d, Martin Boekerd1
a
Institute for Distance Learning (Medical Informatics),
Beuth University of Applied Sciences Berlin, Germany
b
Institute of Medical Informatics, Statistics and Documentation,
Medical University of Graz, Austria
c
Department of Computer Science, University of Freiburg, Germany
d
Institute for Medical Biometry and Medical Informatics & Comprehensive Cancer
Center, Faculty of Medicine and Medical Center, University of Freiburg, Germany
Abstract. 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.
Keywords. Ontology, TNM classification, pancreas tumour, SWRL rules, TNM-O
1. Introduction
The TNM classification has become the accepted basis of cancer staging [1]. The
system has undergone several revisions, with the 7th edition released in 2009 and the 8th
one in 2017. TNM supports treatment planning, prognosis, evaluation of treatment
results, exchange of information between different participants in the treatment process
1
Corresponding Author: Martin Boeker, Institute for Medical Biometry and Statistics, Medical Center
– University of Freiburg, Faculty of Medicine, Stefan-Meier-Str. 26, 79104 Freiburg i. Br., Germany; E-mail:
martin.boeker@imbi.uni-freiburg.de
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 post-
surgical 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.
Table 1. Coding rules for pancreas tumours in TNM7 and TNM8 (slightly abbreviated). Rules for the codes
TX, T0, NX, N0 and M0 are not listed here, as they are identical in both TNM versions. [2], [3]
Code Pancreas TNM7 Exocrine Pancreas TNM8, Neuroendocrine Pancreas
(incl. neuroendocrine TNM8 (grade 1 and 2)
pancreas tumours of grades
not 1 and 2)
Tis Carcinoma in-situ Carcinoma in-situ --
T1 Confined to pancreas, size size max 2 cm* size max 2 cm*
max 2 cm
T1a -- size max 0.5 cm* --
T1b -- size 0.5-1 cm* --
T1c -- size 1-2 cm* --
T2 Confined to pancreas, size 2-4 cm* size 2-4 cm*
size > 2 cm
T3 Invades structures beyond size > 4 cm* size > 4 cm* or invades
pancreas, but not coeliac axis duodenum or bile duct
or superior mesenteric artery
T4 Invades coeliac axis or Invades coeliac axis or Perforates visceral peritoneum
superior mesenteric artery superior mesenteric artery or (serosa) or invades other or-
common hepatic artery gans/neighbouring structures
N1 Metastatic regionary lymph 1-3 metastatic regionary Metastatic regionary lymph
nodes present lymph nodes nodes present
N2 -- >=4 metastatic regionary --
lymph nodes
M1 Distant metastasis present Distant metastasis present Distant metastasis present
M1a -- -- - in liver
M1b -- -- - in other organ
M1c -- -- - in liver and in other organ
*: includes invasion of peripancreatic soft tissue
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.
2. 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].
3. 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.
Figure 1. Modular structure of the TNM ontology as described in the text, explaining how the SWRL rules
are used to re-classify an individual tumour.
Another approach without SWRL rules, implementing the mapping rules using a bridge
ontology with additional subclass relations or other axioms, is also under evaluation.
4. 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 TNM-
codes 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.
Acknowledgements
This work was conducted using the Protégé resource, which is supported by grant
GM10331601 from the National Institute of General Medical Sciences of the United
States National Institutes of Health.
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