AN ONTOLOGY FOR SUPPORTING CLINICAL RESEARCH

ON CERVICAL CANCER

Manolis Falelakis

1,2

, Christos Maramis

1,2

, Irini Lekka

, Pericles A. Mitkas

1,2

and Anastasios Delopoulos

1,2

Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece

Informatics and Telematics Institute, Centre for Research and Technology Hellas, Greece

School of Medicine, Aristotle University of Thessaloniki, Greece

Keywords:

Medical ontology, Cervical cancer, Domain modelling.

Abstract:

This work presents an ontology for cervical cancer that is positioned in the center of a research system for

conducting association studies. The ontology aims at providing a uniﬁed ”language” for various heterogeneous

medical repositories. To this end, it contains both generic patient-management and domain-speciﬁc concepts,

as well as proper uniﬁcation rules. The inference scheme adopted is coupled with a procedural programming

layer in order to comply with the design requirements.

1 INTRODUCTION

Medical Knowledge, being the compilation of many

years of research, has grown vast both in volume

and in complexity. Recently, the need for employing

semantically-aware models of medical knowledge

has become evident. Since then, ontologies have

been successfully used in various medical domains,

disciplines and even aspects of medical practice

and research. Examples of this include the Gene

Ontology (Ashburner et al., 2000), the ReMINE

ontology for adverse events (http://www.remine-

project.eu/), the Ontology of Clinical Re-

search (http://rctbank.ucsf.edu/home/ocre.html),

the Ontology for Clinical Investigators

(http://www.bioontology.org)etc.

This paper presents an ontology designed to

facilitate research in the domain of cervical cancer

(CxCa). This is, to the best of our knowledge, the

ﬁrst ontology to deal with concepts of the CxCa

domain. It’s main targets are (i) to provide a means

for uniﬁcation of various result coding formats and

(ii) to extract implicit knowledge in order to produce

potential query terms as inferred types of individuals.

Through this process, medical researchers are given

the ability to form patient groups, large enough to

provide statistically signiﬁcant results in association

studies.

The paper is structured as follows: In section 2

we describe the problem, while in section 3 we try

to provide some intuition about the CxCa domain.

Section 4 is devoted to the description of the basic

structure of the proposed ontology, section 5 outlines

the medical uniﬁcation rules and section 6 the infer-

encing procedure followed. Section 7 contains the

technologies utilized for implementation and some

ontology statistics and section 8 concludes the paper.

2 PROBLEM STATEMENT

In an effort to gain a more comprehensive and holis-

tic insight on the origin of complex diseases, genetic

association studies (Hirschhorn and Daly, 2005) con-

stitute a signiﬁcant approach for clinical researchers.

In order to perform statistically meaningful associ-

ation studies, large amounts of clinical data are re-

quired - especially when performing studies among

many study factors. However, the current clinical

practice of constructing disposable and isolated clin-

ical research repositories hinders the construction of

collections big enough to facilitate the execution of

complex association studies. To tackle this problem,

comes the uniﬁcation of existing medical repositories

that contain heterogeneous cervical cancer related in-

formation.

The need to resolve this heterogeneity, makes the

ontological representation of the CxCa knowledgethe

perfect candidate solution to this type of problems.

103

Falelakis M., Maramis C., Lekka I., A. Mitkas P. and Delopoulos A. (2009).

AN ONTOLOGY FOR SUPPORTING CLINICAL RESEARCH ON CERVICAL CANCER.

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 103-108

DOI: 10.5220/0002265701030108

 SciTePress

CxCa Ontology

Data Mapping

(Syntactic Unification)

Inference

(Semantic Unification)

Association Study &

Statistical Analysis

Legacy

Archives

Asserted

Knowledge

Inferred

Knowledge

Results

Figure 1: The Context of use of CxCa Ontology.

Ontologies have been widely used as unifying

models to deal with heterogeneity issues of legacy

archives. Furthermore, reasoning on top of ontologies

is a very active ﬁeld of ongoing research that has al-

ready produced fruitful and concrete results (Baader

et al., 2006), (Tsarkov and Horrocks, 2006), (Hus-

tadt et al., 2004), (Dyckhoff, 2000), (Sirin et al.,

2007). Finally, there are plenty of semantic querying

languages suitable for querying on ontologies (Jeen

et al., 2004).

The ontology presented here has been developed

within the context of ASSIST (Mitkas et al., 2008),

a research project funded by the European Commis-

sion. The main objective of this project is the seman-

tic uniﬁcation of physically isolated and heteroge-

neous medical repositories that include cervical can-

cer related data into one semantic repository in order

to facilitate the execution of association studies. In

this context, the CxCa ontology was built to serve as

the schema of a ’container’ repository that enables the

above uniﬁcation.

The resulting data are employed by the project to

perform association studies in an automatic way. The

context of use of the CxCa Ontology is depicted in

Figure 1.

3 DOMAIN BACKGROUND

In this section we present some basic notions regard-

ing the domain and try to provide the reader an insight

about the ontology design requirements.

Cervical cancer is the second leading cause of

cancer-related deaths after breast cancer for women

between 20 and 39 years old (Landis et al., 1999) and

one of the leading types of cancer affecting women

worldwide. Despite a signiﬁcant progress in early

diagnosis and treatment of cervical cancer, there are

more than 60,000 new cases and 30,000 deaths each

year in Europe alone. Recently, it has been proved

that infection by the human papilloma virus (HPV)

is necessary condition for the disease (Walboomers

et al., 1999). However, since HPV infection is highly

unlikely to be the sole cause for developingcancer, re-

cent trends in medical research combine genetic with

clinical data and attempt to discover underlying as-

sociations of the disease with environmental agents,

virus characteristics and genetic attributes, in order to

identify new markers of risk, diagnosis and prognosis.

Diagnosis of CxCa is based mainly on three types

of examinations, namely cytology, colposcopy, and

histology. A problem that arises here is that examina-

tions results are often encoded in Hospital Informa-

tion Systems (HIS) using different coding standards,

and/or custom formats.

On these grounds, a uniﬁcation procedure is es-

sential. For medical research purposes (ie, for con-

ducting association studies), four levels of discretiza-

tion have been considered adequate for each exami-

nation (Agorastos et al., 2009). These are presented

in table 1.

Furthermore, in order to combine the ﬁndings of

these types of examinations, the severity of the dis-

ease is again quantized into four discrete levels, a

number that was considered adequate to capture it’s

different stages (Agorastos et al., 2009). When the

aggregate result needs to be based on more than one

types of examinations, the following practice is as-

sumed. If a histology has been conducted, then it’s re-

sult is considered regardless of the existence of other

results. In other words, histology is regarded the

”golden standard” examination having the highest re-

liability. Then follow colposcopy, and, ﬁnally, cytol-

ogy.

Another important aspect of the clinical research

procedure is that when more than one examinations of

the same type have been conducted, the ”worst” (ie,

most severe) is considered valid.

The same thing holds when a patient is associated

with more than one cases (ie, various series of exami-

nation and treatment procedures at different moments

in the course of time). Again, for this person, only

KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development

104

Table 1: Summary of the adopted classiﬁcation scheme for CxCa stages.

Normal (+Within Normal Limits) stage 0

Low Grade Cervical Intraepithelial Neoplasia (LCIN) stage 1

High Grade Cervical Intraepithelial Neoplasia (HCIN) stage 2

Invasive Cervical Cancer stage 3

it’s ”worst” case is taken under consideration for the

execution of an association study.

4 ONTOLOGY STRUCTURE

The conceptual schema of CxCa Ontology basically

consists of two types of entities (concepts).

1. Patient Management Entities. These are generic

entities capturing information about patient

records as they are stored in the corresponding

Hospital Information Systems (HIS). Their orga-

nization is based on the concepts of Case and

Visit.

A Case can be deﬁned as ”a collection of data re-

ferring to a patient for a certain period of time,

within which a diagnosis on the disease is mean-

ingful, i.e., makes medical sense” and is related

to a Person. Each Case comprises of one or more

Visits. Every Visit is essentially a medical record

and may contain one or more Medical Interven-

tions. Each Medical Intervention is associated

with a single Result. However, since within the

context of a Case multiple Medical Interventions

of the same type with conﬂicting Results may be

conducted, each Case is associated with one and

only one Collective Result for each type of Medi-

cal Intervention.

When a diagnosis is made (i.e. the Severity Index

of the disease is calculated) it refers to a single

Case, taking into consideration all the Visits and

interventions the latter may be associated with.

This means that each Person is possibly related

with more than one Cases with different Diag-

noses through the hasCase object property. How-

ever, since for the purpose of performing an asso-

ciation study only one Case per Person has to be

considered, the hasWorstCase object property as-

sociates each Person with its Case with the worst

Diagnosis. A new Case is instantiated should the

patient return after a long period of time, which

would yield any previous examinations obsolete.

This type of entities also includes notions such

as Clinic, Medical Intervention, Result, Lifestyle

Choice etc.

2. Domain Speciﬁc Entities. These model terms that

are strongly associated with the CxCa disease, it’s

stages and the related interventions and genotypic

and phenotypic factors. This type of entities also

contains concepts that do not directly correspond

to the information stored in the EHR and are es-

sentially inferred through a reasoning process ac-

cording to appropriate deﬁnition rules.

Examples of such entities include Colposcopy,

HPV Vaccination, MTHFR C667T Polymorphism,

Stage1 CxCa Severity Index etc.

The scheme adopted is designed to be generic enough

to be potentially able to incorporate information about

other types of disease as well. The key-concepts of it,

along with their interconnecting properties, are repre-

sented in Figure 2.

Furthermore, the notion that the stages of the dis-

ease and the risk factors under investigation are not

affected by interrelations between different patients,

motivated us to design the ontology so that the ABox

forms independentindividual groups. This means that

two Person entities are neither directly nor indirectly

connected to each other. In this way, reasoning can be

performed independently within each subgraph con-

cerning a person, making the reasoning process an

explicitly parallel task.

5 MEDICAL UNIFICATION

RULES

The uniﬁcation tasks of the ontology can be roughly

divided into two categories. The ﬁrst category trans-

lates results from various different coding schemes to

a common format, while the second reveals implicit

classiﬁcation criteria.

5.1 Result Uniﬁcation Rules

The ontology contains all major standards for each

diagnostic intervention. Their results are translated

to a 4-category classiﬁcation scheme for each type of

examination. For instance, in cytology, the Munich,

Bethesda and Pap coding schemes are considered and

characterization of a Cytology Result as a Stage0 Cy-

tology Result is based on satisfaction of the restriction

posed in formula (1).

AN ONTOLOGY FOR SUPPORTING CLINICAL RESEARCH ON CERVICAL CANCER

105

Person

Visit

HVP Test

Cytology

Histology

Case

Visit

hasCase

hasVisit

hasIntervention

Histology Result

CxCa Severity

Cytology Result

HPV Severity

hasResult

hasSeverity

Inferred Types

Smoking

Use of

Contraceptives

hasLifestyleChoice

Polymorphism

hasPolymorphism

Smoking Severity

hasSeverity

Asserted Types

Figure 2: Basic Entities and their Interconnecting Properties.

stage0 Cytology Result ≡ Cytology Result ⊓

(∃isResultOfExam

(Cytology ⊓

((∃hasCytologyPapanikolaouResult {Class

II}) ⊔

(∃hasCytologyPapanikolaouResult {Class

I}) ⊔

(∃hasCytologyMunichResult {I}) ⊔

(∃hasCytologyMunichResult {II})⊔

(∃hasCytologyBethesdaResult {negative}) ⊔

(∃hasCytologyBethesdaResult {trichomonasVaginalis}) ⊔

(∃hasCytologyBethesdaResult {fungalOrganismsMorphConsistentWithCandidaSpp}) ⊔

(∃hasCytologyBethesdaResult{shiftInFloraSuggestiveBacterialVaginosis}) ⊔

(∃hasCytologyBethesdaResult{bacteriaMorphConsistentWithActinomycesSpp}) ⊔

(∃hasCytologyBethesdaResult{cellularChangesConsistentWithHerpesSimplexVirus}) ⊔

(∃hasCytologyBethesdaResult{otherNonNeoplasticFindings}) ⊔

(∃hasCytologyBethesdaResult{reactiveCellularChangesAssocWithInﬂammation}) ⊔

(∃hasCytologyBethesdaResult{reactiveCellularChangesAssocWithRadiation}) ⊔

(∃hasCytologyBethesdaResult{reactiveCellularChangesAssocWithIUD}) ⊔

(∃hasCytologyBethesdaResult{glandularCellsPostHysterectomyOrTrachelectomy}) ⊔

(∃hasCytologyBethesdaResult{atrophy}) ⊔

(∃hasResultDataType{stage0})

)

) (1)

5.2 Diagnostic Uniﬁcation Rules

This type of rules aims at producing aggregate selec-

tion criteria for patient record retrieval.

An indispensable selection criterion when per-

forming association studies is the Severity Index (i.e.

the stage) of the disease. As there exist three types of

diagnostic interventions that may be associated with a

Case in our domain, calculation of the severity of the

speciﬁc Case is based upon them, giving priority to

existence of a Histology, in absence of which, consid-

ering Colposcopy and if both of the former are absent,

Cytology is considered.

As Description Logics adhere to a strict open

world assumption, it is impossible to deduce the ab-

sence of an examination result, as knowledge is con-

sidered incomplete. We overcome this problem by in-

stantiating ”triplets” of results (one for each exami-

nation) and explicitly storing a ”NoResult” property

for the examinations that have not actually been con-

ducted. An example of such a rule is given in formula

(2).

6 INFERENCE

Medical inference is essentially based on incomplete

knowledge and is thus non-monotonic. Doctors sug-

gest a treatment conjecturing about the most prob-

able cause for some observed symptoms or exami-

nation results, disregarding theoretically possible but

unlikely alternative causes. Moreover, these results

may contradict one another.

KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development

106

stage1 Severity ≡ Cervical Cancer Severity ⊓

(∃isDiagnosedBy

(Case ⊓

((∃

caseHasCollectiveResult{ stage1 Collective Histology Result}) ⊔

((∃

caseHasCollectiveResult{ NO Collective Histology Result}) ⊓

(∃

caseHasCollectiveResult{ stage1 Collective Colposcopy Result})) ⊔

((∃

caseHasCollectiveResult{ NO Collective Histology Result}) ⊓

(∃

caseHasCollectiveResult{ NO

Collective Colposcopy Result}) ⊓

(∃

caseHasCollectiveResult{ stage1 Collective Cytology Result}))

)

) (2)

This is also the case in our setting. Before con-

cluding to a triplet of results associated with a case in

order to apply the rules of section 5.2, one has to pro-

duce a Collective Result for each examination type,

taking into account all examinations of this type.

Because there may be an undeﬁned number of

each one of them, setting an upper limit of instances,

creating all of them and applying the trick of ”no re-

sult” as in section 5.2 is not an option here. In order

to overcome this problem we choose to add an inter-

mediate external inference step and consider closed

world semantics by using appropriate queries in an

RDF query language. This procedural layer executes

sequentially a number of queries, implementing the

set difference operator, in order of decreasing sever-

ity.

The process for doing this for cytology is as follows:

• First we query for cases containing cytologies as-

sociated with a stage3 Cytology Result. Obvi-

ously, since this is the worst outcome, these are

considered the Collective Cytology Results for the

corresponding cases.

• Then we query for examinations associated with

a stage2 Cytology Result. This query also returns

the Cytologies of the previous query, which are

subtracted with a proper query, and the remain-

ing ones are considered as the Collective Cytol-

ogy Results for the corresponding cases.

• The steps are continued for the other two stages

and the process is repeated for all examinations.

We employ the same methodology in order to

identify the worst Case for each Person. Again, con-

secutive queries are employed, this time retrieving

Persons w.r.t. their Collective Severity (as calculated

with the rules of section 5.2), in order of decreasing

severity.

In summary, the uniﬁcation procedure consists of

the steps depicted in Figure 3.

We have to point out that, because of it’s non-

monotonic nature, this process must be repeated each

time the ontology ABox is populated with new data.

Step 1:

Mapping to Ontology terms (Abox Instantiation)

Step 3

Calculation of Aggregate Metrics – Query Terms

Step 2:

Result Unification (through DL Reasoning)

Step 3.1:

Case-based Collective Result Calculation (Functional Programming)

Step 3.3:

Patient-based Worst Case Calculation (Functional Programming)

Step 3.2:

Case-based SI Calculation (through DL Reasoning)

Figure 3: The Uniﬁcation Procedure.

However, this does not yield problems for our pur-

pose, as the system is research-oriented and does not

need to be concurrent w.r.t. the EHR records of the

associated hospitals.

7 IMPLEMENTATION DETAILS

The Ontology was developed in Protege knowledge

modeling tool (Noy et al., 2001), while the knowl-

edge representation language employed was OWL-

DL. Sesame (Broekstra et al., 2002) and OWLIM

(Kiryakov et al., 2005) were chosen for storage and

reasoning, respectively. DL reasoning is performed

by the IRRE component of Sesame and the queries

are expressed in SeRQL (Broekstra and Kampman,

2003).

The ontology TBox currently consists of 174

classes, 22 object and 96 datatype properties, 26

equivalent class axioms, while current instantiation of

the ontology ABox includes about 680,000 individual

620,000 role assertions, containing data about 3,200

patients and 8,400 cases.

AN ONTOLOGY FOR SUPPORTING CLINICAL RESEARCH ON CERVICAL CANCER

107

8 CONCLUSIONS

We have described the basic structure and inference

mechanisms of a medical ontology in the domain of

cervical cancer. The ontology is the central compo-

nent of a system that aims at the uniﬁcation of various

virtual medical repositories and acts as a common lan-

guage designed as a means for conducting association

studies. Limitations and requirements of this cause

have made the use of an ad-hoc reasoning scheme in-

evitable.

ACKNOWLEDGEMENTS

This work was supported by the IST-2004-027510

project ‘Association Studies Assisted by Inference

and Semantic Technologies (ASSIST)’, funded by the

Commission of the European Community (CEC).

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