=Paper=
{{Paper
|id=Vol-1442/paper_6
|storemode=property
|title=An Ontology for TNM Clinical Stage Inference
|pdfUrl=https://ceur-ws.org/Vol-1442/paper_6.pdf
|volume=Vol-1442
|dblpUrl=https://dblp.org/rec/conf/ontobras/MassicanoSTONP15
}}
==An Ontology for TNM Clinical Stage Inference==
https://ceur-ws.org/Vol-1442/paper_6.pdf
An Ontology for TNM Clinical Stage Inference
Felipe Massicano2 , Ariane Sasso1 , Henrique Amaral-Silva1 , Michel Oleynik3 ,
Calebe Nobrega1 , Diogo F. C. Patrão1
1
CIPE - A. C. Camargo Cancer Center
2
IPEN - USP
{djogo,ariane.sasso,henrique.silva,cnobrega,michel}@cipe.accamargo.org.br
massicano@gmail.com
Abstract. TNM is a classification system for assessment of progression stage of
malignant tumors. The physician, upon patient examination, classifies a tumor
using three variables: T, N and M. Definitions of values for T, N and M de-
pend on the tumor topography (or body part), specified as ICD-O codes. These
values are then used to infer the Clinical Stage (CS) and reflect the disease pro-
gression, which can be 0 (no malignant tumor), IS (in situ), I, II, III, or IV. The
rules for inference are different for each topography and may depend on other
factors such as age. With the objective of evaluating missing CS information
on A. C. Camargo Cancer Center databases, we developed an open ontology to
represent TNM concepts and rules for CS inference. It was designed to be easily
expansible and fast to compute.
1. Introduction
Originally developed in 1958 and since then maintained by the Union for International
Cancer Control (UICC), the TNM staging system is a cancer classification scheme used
mainly to predict survival rates given the disease severity. Based on the fact that patients
with localized tumors present higher survival rates when compared to patients with distant
metastasis, the TNM staging system aims to help doctors with treatment planning, disease
prognosis, interpretation of treatment results and also to facilitate information sharing and
improve cancer research [Sobin and Wittekind C 2002].
The classification is based on three main discrete variables: T (0-4), for the eval-
uation of the primary tumor extension; N (0-3), for the appraisal of the presence and the
extension of metastasis in regional lymph nodes; and M (0-1), to annotate the absence
or presence of distant metastasis. Some topographies include an additional character in
the range a − d for specifying subcategories. Additional characters can also be included
to define the information source (clinical exam or pathology biopsy); the diagnosis stage
(before/after treatment, after recurrence or through autopsy); and the existence of multi-
ples tumors in the same site. Moreover, other symbols describe optional lymphatic and
venous invasion, the histological grade, the metastasis site, presence of isolated tumor
cells, sentinel lymph node invasion status, the degree of certainty and the presence of
residual tumor after the treatment [Sobin and Wittekind C 2002].
Additionally, each topography has rules for mapping the TNM staging into one
variable called clinical stage. The clinical stage ranges from 0 to IV, with an additional
character for some sites. Although rules differ for each topography, higher clinical stages
correlates with worse prognosis. Therefore, its determination is a central point in the
cancer diagnostic process.
The rules for clinical staging inference, standardized by the TNM staging system,
should be used by the physicians during the medical appointment; however, many factors
contribute to this not being largely adopted, such as: resistance by physicians to extra
paperwork, physicians uncertainty concerning the current staging system and lack of reg-
ulatory processes to enforce compliance with the standard [Schmoll 2003]. Many efforts
have been made lately to reach that, including its recommendation by specialized medical
societies and its use as a mandatory prerequisite for quality accreditation on oncology
care [Neuss et al. 2005].
Moreover, the TNM staging information is also crucial for cancer research. As
the different clinical stages indicates better or worse response to certain treatments and
better or worse prognosis, cancer studies usually focus on diseases of a specific tissue, and
a specific clinical stage. If the clinical database does not contain this information for a
relevant fraction of the patients, the researchers may have to resort to manually assessing
the patient records to find out the sample size.
Since the rules for clinical stage coding are explicitly defined in the TNM publi-
cation, it is possible to create a computer program to automatically evaluate them. Such a
program would validate existing values, or even provide this information when it is miss-
ing. However, representing all rules directly on a computer programming language is an
extenuating and repetitive task, and may lead to code maintenance issues. In addition, it
would be difficult to a oncology expert, untrained in computer programming, to validate
the algorithms.
In order to overcome these difficulties, a proposal to model the concepts, descrip-
tions and rules in TNM clinical stages is to use ontologies. In summary, the term ontology
means a specification of a conceptualization and it has been applied to create standardized
dictionaries in several fields. [Gruber 1993].
Standardized ontologies have been developed in many areas in such a way that
domain experts can share and annotate information in their respective fields. In medicine,
well-known standardized and structured vocabularies such as Systematized Nomenclature
of Medicine–Clinical Terms (SNOMED CT) 1 , RadLex [Langlotz 2006], Unified Medi-
cal Language System (UMLS) [Lindberg et al. 1993], Medical Subject Headings (MeSH)
[Nelson et al. 2001] and others have been used for clinical and research purposes. Al-
though new general and specialized ontologies are emerging fast, there is no published
ontology yet that approaches the TNM clinical stage coding problem. Yet, some ontolo-
gies may represent some of the TNM concepts.
The National Cancer Institute Thesaurus (NCIt) is a reference terminology that
covers the clinical care, basic and translational research, public data and also the admin-
istrative domain regarding the National Cancer Institute (NCI). It was built upon the NCI
Metathesaurus from the UMLS and it is based on description logic with relationships be-
tween semantically rich concepts [Smith et al. 2005]. It is coded on OWL Lite, a subset of
OWL-DL with enough complexity to represent the ontology data [Bechhofer et al. 2004].
It provides some of the TNM concepts for 6th and 7th edition and each topography has its
1
http://www.ihtsdo.org/snomed-ct
own T, N, M and CS classes with annotations in English. When a concept has the same
definition in the 6th and 7th edition, it is defined as a single class, or else specific classes
for each version are defined. There is no definition of axioms for inference of Clinical
Stage based on values of T, N and M.
The SNOMED CT is a vocabulary comprising more than 310.000 concepts hi-
erarchically organized. There are concepts to represent all TNM (including individual
definitions for T, N, M and CS for each topography), however, there are no compositional
rules connecting the T, N, M and the topography to the CS. Moreover, its license is not
open and there is no official or non-official translation to Portuguese.
Dameron et al. propose the creation of an ontology for automatic grading of lung
tumours using OWL-DL description logic language, inspired by the controlled vocabu-
lary for cancer, the NCIt and also by the Foundational Model of Anatomy (FMA) for its
anatomical decomposition [Dameron et al. 2006]. Marquet et al. also developed an on-
tology based on the NCIt for automatic classification of glioma tumors using the WHO
grading system. Their ontology contained 243 classes (234 of them corresponding to
NCIt classes) which correctly classified simulated tests and graded correctly ten clinical
reports out of eleven used on the test for clinical data [Marquet and Dameron 2007]. The
links mentioned on both manuscripts for downloading the ontologies were not active at
the time of this writing.
The TNM ontology [Boeker et al. 2014] is a thorough representation of the TNM
concepts for breast cancer using OWL-DL with SRI expressivity. The focus there was
representation of the clinical meaning of each concept: T, N and M, with links to the
Foundational Model of Medicine [Rosse and Jr. 2003]. They depict how to represent the
tumor, the lymph node, distant metastasis, the organ locations specified and the tumor
invasion pattern. Complete as it is, there is no rules for inference of clinical stage, nor the
concepts related to the latter.
In this work we present an ontology for allowing inference of the TNM clinical
stage of tumors, based on given values of T, N, M, the ICD-O topographic code and other
information. This ontology should provide annotations with the original descriptions from
the reference, and links to the NCIt ontology wherever applicable.
2. Materials and Methods
The first step was to identify the most common topographies on A. C. Camargo Cancer
Center patients. Upon interview with an oncologist expert, we created a list of the ten
most relevant topographies for research on this institution. We used the TNM 6th edition,
because most of the relevant databases in the institution used this version of the coding
system.
To achieve the goal of a fast-computing ontology, we kept its expressivity at the
bare minimum while preserving the intended meaning of concepts. We used only sub-
class, intersection, equivalence, disjunction between classes, and object properties. As
seen on Figure 1, the ontology is divided in four files (Figure 1): the main ontology, with
the general TNM concepts and the imports of all others; the ICD-O topography, with the
topographic classes referred by the TNM; a file with the annotations and finally a file with
the clinical stage inference axioms.
Figure 1. TNM Ontology components and imports diagram.
The concepts for representing T, N, M and CS were created as an hierarchy of
classes; the root concept TNM 6th edition, and its direct subclasses T, N, M and EC (the
portuguese acronym for CS). There are subclasses that describes the general classification
for all tumors, according to the introduction of the TNM reference. There may be an
additional level of subclasses for representing concepts such as T1b or CS IIIa (as defined
in some topographies such as breast cancer). We called all those the general staging
classes. See Figure 2.
Figure 2. Class hierarchy for TNM concepts.
As the clinical stage rules depends on the tumor topography, the axioms for infer-
ence would need reference to ICD-O topography concepts. We could not find any ICD-O
ontology available, and it was beyond the scope of our work to create one. However,
as ICD-O topographic codes were based on ICD-10 cancer codes, we reused an ICD-10
ontology, available on the BioPortal2 . We kept only the C00-C80 range of codes, re-
moved some undefined codes within this range (such as C43, C78 and C79) and added
C42 (as described in the ICD-O introduction). We also changed the ontology namespace
and changed the label annotation property to skos:label. Reference to the prior ontology
was kept. In Figure 3 there is a depiction of the ICD-O ontology.
To represent actual patient data, there should be an instance of class Patient, re-
lated to one or more instances of class tumor. In order to use the ontology to represent
data, an instance representing the tumor should be created and related to subclasses of T,
N, M, CS and ICD-O Topography classes. Following the TNM guidelines for staging, a
patient with two primary tumors should be represented as one instance of a patient linked
to two tumor instances; however, a patient with one tumor that metastasised should have
only one tumor instance. The patient instance should be linked to the tumor instances by
an object property.
A tumor should not belong to more than one topography class. First, it does not
make clinical sense: a tumor should be located on a specific location or organ. It may
2
http://bioportal.bioontology.org/ontologies/ICD10
Figure 3. Class hierarchy for ICD-O concepts.
happen to spread itself to neighbour tissues or the precise location maybe be dubious
(such as the gastroesophageal junction). In these cases the most probable tumor location
should be selected and linked to the instance. The ICD-O Topography ontology states
disjunction axioms for all their classes, preventing a tumor instance to belong to two
topographic locations at once.
As each topography has different definitions for individual values of the general
staging classes T, N and M, we created a script to parse a text file and create a RDF/XML
file defining specific staging classes and inference axioms for a pair of T, N or M val-
ues and one topography, plus annotations using rdf:Description annotation property. We
manually created text files based on the TNM definitions. The axioms are subclasses re-
lating the specific staging classes to the intersection of one general staging class and one
topography class.
Whenever a corresponding NCIt concept was available, it was linked to the spe-
cific staging class by the property owl:equivalentTo (see Figure 4). Not all concepts de-
fined on TNM were present on NCIt, for instance, the T4 for Breast Cancer.
C50 u M 1 v C50 M 1 ≡ N CIt : C49009
Figure 4. Relation between an annotation from the current ontology and a NCIt
class.
The standard procedure at the A.C. Camargo Cancer Center is to encode the TNM
staging and the ICD-O topography during clinical attendance. As a result, structured
information about the clinical stage is not promptly available in its databases. Based on
this, we use the previously constructed inference axioms that considered the values of T,
N, M and ICD-O to infer the clinical stage (CS) values.
The format starts with a first line containing the name of the determined clinical
stage class. The second line contains one or more topography classes, which are linked to
that clinical stage class and separated by a space character. The other lines have a relation
of conjunction between the group T, N and M with each specified ICD-O topography. See
Figure 5 for an excerpt of these axioms.
C50 u T is u N 0 u M 0 v BreastCancer CS 0
C50 u T 1 u N 0 u M 0 v BreastCancer CS I
C50 u T 2 u N 1 u M 0 v BreastCancer CS IIB
C50 u N 3 u M 0 v BreastCancer CS IIIC
C50 u M 1 v BreastCancer CS IV
Figure 5. Axioms for inference of clinical stage (CS) based on ICD-O topography
and T, N and M classes.
For testing purposes we created another ontology with subjects and patients and
assignments to specific classes of this ontology. For each subject we included a topo-
graphic class which includes the TNM for each test according to the example below.
patientT est00100 : P atient u hasT umor value patientT est00100 T umor1
patientT est00100 T umor1 : C50 u T is u N 0 u M 0
After the inference, we can check the TNM annotation classes and also the respec-
tive NCIt code class. Thus we reach the ontology objective informing the inferred class
to their respective clinical staging. We created a script to generate 566 tests based on the
text mappings, as instances of Patient class with exactly one Tumour instance related to
it. There were one test for each possible combination of T, N, and other variables for
which could be inferred a clinical stage. We created then two queries, one for assessing
test instances without any clinical stage inferred (it should have none) and other listing
the inferred plus the expected clinical stage for each test.
The software we used to create the ontologies was Protégé 3 . The scripts for the
creation of OWL files based on text files were developed in Python. The inferences were
computed using Pellet4 .
3. Results
The resulting TNM ontology is divided in four files: main TNM concepts, ICD-O to-
pography, annotations and clinical stage axioms. The main TNM ontology contains the
3
http://protege.stanford.edu/
4
https://github.com/complexible/pellet
general staging classes and includes the other ontologies. The ICD-O topography ontol-
ogy contains the topographic codes and superclasses (such as C00-C14 - Head and Neck),
with English descriptions. The annotation ontology define the specific TNM classes (such
as C50 T1 and C61 M0) and their corresponding description in Portuguese and English.
Finally, the clinical stage axioms ontology define the logical axioms that allows the infer-
ence of clinical stage based on ICD-O topography and TNM values.
The consolidated ontologies have ALC (Attributive Concept Language with Com-
plements) expressivity. It consisted of 4.382 axioms, 2.954 logical axioms, and 772
classes. It defines 1.690 subClassOf axions, 16 EquivalentTo axioms, 1.248 disjoint-
Classes axioms and 643 AnnotationAssertion axioms. The ontology, the scripts and the
text files used to generate it were released under the APACHE-2.0 5 open source license
and are available online at
https://github.com/djogopatrao/tnm_ontology/tree/master/
ontologies
All 566 test instances were assigned a clinical stage, and only one was assigned
two clinical stages. P atientT est 51 was supposed to be assigned Prostate Cancer Clini-
cal Stage I, however an additional concept, Clinical Stage II, was present. This is because
the definition of those clinical stages, as stated on the original reference, is ambiguous;
Clinical Stage I is defined as T1a, N0, M0 and G1 (Gleason 2-4, discreet anaplasia), while
Clinical Stage II, among other definitions, can be T1, N0, M0 and any G. T1, for prostate
cancer in the 6th edition of TNM, means ”Clinically inapparent tumor neither palpable
nor visible by imaging, while T1a (a subconcept for the former) is defined as ”Tumor
incidental histologic finding in 5% or less of tissue resected. Therefore, as T1a is also T1,
so Clinical Stage II is also applicable, and the definition of Clinical Stages in the prostate
section of TNM 6th edition contained an ambiguity, detected by means of the ontology.
4. Discussion
We successfully represented the desired TNM rules using an ontology with a simple ex-
pressivity profile. That will allow the classification of tumors to remain computable.
The NCIt and SNOMED CT ontologies provide the general concepts involved
with tumor staging: the values and description for T, N, M and CS for each topography.
However, NCIt does not contains all codes for all topographies. SNOMED CT, in the
other hand, does not define which TNM edition their concepts refer to. Neither defined
axioms for inferring the clinical stage.
The work by Dameron et al. focus at the anatomical decomposition of a single
topography, whereas the present work approaches several topographies, focusing on in-
ference of clinical stage. Besides that, there is no description of the final ontology in the
mentioned paper and the links provided are not available [Dameron et al. 2006].
In the paper by Boeker et al, a very detailed description of breast cancer TNM
definitions is formalized in a very expressive ontology. The main objective of their work
seems to be the formal representation of clinical examination findings for each value of
T, N and M, with links to the anatomical and tumoral invasion patterns concepts. That
5
http://www.apache.org/licenses/LICENSE-2.0
allowed the analysis of inconsistencies and inaccuracies in the definitions of TNM it-
self [Boeker et al. 2014]. However, the ontology at the time of this writing does not
include the clinical stage classes, and thus does not provide axioms for their inference.
Moreover, this ontology high level of expressivity (SRI) would arguably be less efficient
than ALC for a given A-Box.
The tests showed that the inference worked as expected, except in one case,
in which the definition provided by the original reference is ambiguous. A related
work [Boeker et al. 2014] also found similar ambiguities; this shows how ontologies can
be used to prevent classification definition errors.
The presented ontology may be applied to perform validation of existing databases
or classify tumors based on TNM values. The usage of relational database to ontology
mapping software [Calvanese et al. 2011] [Bizer 2004] [Cullot et al. 2007] allows the us-
age of the present ontology and inference tools on relational databases, the de facto indus-
try standard. As it provides annotations for the meaning of individual T, N and M values
for each topography, it may also serve as a reference for physicians and cancer registry
workers.
As future work, the presented ontology may be completed to include all to-
pographies and alignment with the NCIt ontology. Alignments with the TNM Ontol-
ogy [Boeker et al. 2014] may also be of interest. Currently, there are annotations in both
Portuguese and English, and other languages may be added. The ontology may be up-
dated to represent the TNM 7th edition, possibly representing an alignment between it
and the 6th edition, which may help database migration efforts. Finally, the pathological
stage and other modifiers (such as stage post treatment) may also be implemented.
5. Conclusion
We showed that the presented ontology accurately represents the descriptions and infer-
ence rules from the selected topographies, fulfilling the main objective of this work. It
may be useful in a number of tasks involving tumor staging. It is open source, allowing
scrutiny and contributions from the scientific community. It has means to be linked to
other TNM ontology efforts and well-established vocabularies, increasing its interoper-
ability. Finally, it is lightweight to compute, being a valuable tool to validate or complete
TNM databases.
References
Bechhofer, S., van Harmelen, F., Hendler, J., Horrocks, I., McGuinness, D. L., Patel-
Schneider, P. F., and Stein, L. A. (2004). OWL Web Ontology Language Reference.
Technical report, W3C, http://www.w3.org/TR/owl-ref/.
Bizer, C. (2004). D2rq - treating non-rdf databases as virtual rdf graphs. In In Proceedings
of the 3rd International Semantic Web Conference (ISWC2004.
Boeker, M., Faria, R., and Schulz, S. (2014). A Proposal for an Ontology for the Tumor-
Node-Metastasis Classification of Malignant Tumors: a Study on Breast Tumors. In
Jansen, L., Boeker, M., Herre, H., and Loebe, F., editors, Ontologies and Data in Life
Sciences, number 1, pages B1–B5, Freiburg.
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro,
M., Rosati, R., Ruzzi, M., and Savo, D. F. (2011). The mastro system for ontology-
based data access. Semantic Web Journal, 2(1):43–53. Listed among the 5 most cited
papers in the first five years of the Semantic Web Journal.
Cullot, N., Ghawi, R., and Yétongnon, K. (2007). Db2owl : A tool for automatic database-
to-ontology mapping. In Ceci, M., Malerba, D., and Tanca, L., editors, SEBD, pages
491–494.
Dameron, O., Roques, E., Rubin, D., Marquet, G., and Burgun, A. (2006). Grading lung
tumors using OWL-DL based reasoning. In 9th Intl. Protégé Conference, pages 1–4,
Stanford, California.
Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowl-
edge Acquisition, 5(2):199 – 220.
Langlotz, C. P. (2006). Radlex: A new method for indexing online educational materials.
RadioGraphics, 26(6):1595–1597. PMID: 17102038.
Lindberg, D. A., Humphreys, B. L., and McCray, A. T. (1993). The unified medical
language system. Methods Archive, 32(4):281–291.
Marquet, G. and Dameron, O. (2007). Grading glioma tumors using OWL-DL and NCI
thesaurus. AMIA Annual . . . , pages 508–512.
Nelson, S., Johnston, W. D., and Humphreys, B. (2001). volume 2 of Information Sci-
ence and Knowledge Management, chapter Relationships in Medical Subject Headings
(MeSH), pages 171–184. Springer Netherlands.
Neuss, M. N., Desch, C. E., McNiff, K. K., Eisenberg, P. D., Gesme, D. H., Jacobson,
J. O., Jahanzeb, M., Padberg, J. J., Rainey, J. M., Guo, J. J., and Simone, J. V. (2005).
A process for measuring the quality of cancer care: The quality oncology practice
initiative. Journal of Clinical Oncology, 23(25):6233–6239.
Rosse, C. and Jr., J. L. M. (2003). A reference ontology for biomedical informatics: the
foundational model of anatomy. Journal of Biomedical Informatics, 36(6):478 – 500.
Unified Medical Language System.
Schmoll, H.-J. (2003). F.l. greene, d.l. page, i.d. fleming et al. (eds). ajcc cancer staging
manual, 6th edition. Annals of Oncology, 14(2):345–346.
Smith, B., Ceusters, W., Klagges, B., Köhler, J., Kumar, A., Lomax, J., Mungall, C.,
Neuhaus, F., Rector, A. L., and Rosse, C. (2005). Relations in biomedical ontologies.
Genome Biol, 6(5):R46–R46. gb-2005-6-5-r46[PII].
Sobin, L. and Wittekind C (2002). Classificação de Tumores Malignos. Wiley and Sons,
New York, 6th edition.