BIOMEDICAL ONTOLOGIES AND GRID COMPUTING

As New Resources for Cancer Registries

Giulio Napolitano

1,2

, Alejandra González Beltrán

, Colin Fox

Adele Marshall

, Anthony Finkelstein

and Peter McCarron

Centre for Clinical and Population Sciences, School of Medicine and Dentistry, Queen’s University Belfast, U.K.

Centre for Statistical Science and Operational Research, School of Maths and Physics, Queen’s University Belfast, U.K.

Department of Computer Science, University College London, UK

Keywords: Cancer registries, Ontologies, Biomedical grid systems, Semantic web.

Abstract: Cancer registry information systems need to deal with several data sets annotated with different coding

systems. Designing, maintaining and linking these datasets involves dealing with semantic issues, tackling

the shortcomings exhibited by coding systems as well as considering an appropriate computing

infrastructure. We argue that biomedical ontologies and a Grid service infrastructure, together with a clear

separation between semantic and coding models, can prove beneficial to cancer registries in terms of

accuracy of knowledge modelling, interoperability and knowledge sharing with other registries and related

data sources, automation of information retrieval. A real-life example is illustrated and a brief review of

related projects is provided. We conclude that a formal semantic layer, which is the basis of large scale

meaning-oriented projects such as the Semantic Web, is the key to the provision of a uniform, science-based

view across cancer registries and related systems.

1 INTRODUCTION

The biomedical domain is characterised by the use

of a variety of information and the continuous

generation of new data. Thus, it is fundamental to be

able to design, maintain and link different data sets.

These data sets could not only reside in distinct

locations but also use a variety of syntaxes, even

when data items have the same meaning or data

items may differ in terms of meaning. Information

systems for cancer registries are no exception with

respect to these characteristics.

Cancer registration constitutes the source of data

on cancer incidence, prevalence and survival rates

(UKACR). In the UK, cancer registries have the

important role of implementing and monitoring the

national initiatives that aim to improve the quality of

care and survival prospects for cancer patients

(UKACR). A consortium of UK cancer registries

recently underwent a re-design of their information

systems (PRAXIS). Within this consortium there is a

desire to standardise the registration practices and

systems. The system is now capable of capturing

generic code items from any coding system for each

tumour record. Presently, the main coding or

terminological systems used for clinical and

pathological information that must be adopted by

the registries include ICD, SNOMED-CT, READ,

OPCS, TNM, plus several other tumour stage and

grade coding systems. These coding systems enable

the recording of all the information the cancer

registries are supposed to collect for epidemiological

purposes. Licensed versions of these coding systems

are defined, distributed and revised by the World

Health Organisation (WHO) and other organisations

and are adopted by healthcare institutions (hospital,

cancer centres, diagnostic units and laboratories).

On the other hand, these coding systems present

several issues. First, there is not a universally

accepted medical terminology that could support a

widespread development of electronic medical

records (Fung, 2005). As terminologies need to fulfil

a wide range of functions (such as direct patient

care, billing, statistical reporting, automated decision

support and clinical research), it is unlikely that a

single terminology could ever be suitable for all the

512

Napolitano G., González Beltrán A., Fox C., Marshall A., Finkelstein A. and McCarron P. (2009).

BIOMEDICAL ONTOLOGIES AND GRID COMPUTING - As New Resources for Cancer Registries .

In Proceedings of the International Conference on Health Informatics, pages 512-517

DOI: 10.5220/0001781705120517

 SciTePress

purposes (Fung, 2005). Moreover, given the basic

requirements of capturing healthcare information

electronically and enabling secondary uses of the

data for different purposes, the mapping between

standard terminologies is a necessity (Fung, 2005).

If we focus on the activity of cancer registration,

additional issues related to coding systems also

include:

 The meanings of codes and terms created

for particular purposes (such as billing)

may be unclear in other contexts (such as

epidemiology) (Cimino, 1996).

 New versions of the systems are

periodically released, and adopted at

different times by different organisations.

Cancer registries need to cope with these

inconsistencies.

 Sometimes the new coding systems are not

backward compatible (e.g. SNOMED 3 is

not backward compatible with

SNOMED2), for instance due to the

reallocation of existing codes to different

meanings (e.g. T57000 is Stomach, NOS in

SNOMED3-T and Gallbladder, NOS in

SNOMED2-T) or because new codes are

created for new kinds of disease (such as

MALT-Lymphoma, M-9699/3 in

SNOMED3-M). Then, semantic mappings

between coding systems are required

(Fung, 2005).

 Linkage to repositories using different

terminologies requires inter-terminology

mappings, which is a complicated process

not easily generalisable.

A related issue, regarding the information

systems, is that validation and Quality Assurance

(QA) rules are hard-coded in the cancer registry

information systems. There is currently no facility to

represent them in an abstract fashion and they even

change between registries. In spite of this, it is noted

that some efforts have been made in the UK to

create a modular 'tumour matching box' to be used

and possibly customised by individual registries

using the new PRAXIS system.

For all the reasons stated above, this paper

advocates building a semantic layer on top of all the

cancer registries’ subsystems requiring to use coding

systems such as ICD9 and ICD10. This semantic

layer combines Semantic Web and Grid computing

technologies. The rationale behind the proposed

approach is explained in the next section. Section 3

introduces a motivating example, while Section 4

presents related work. We conclude with a

discussion and a plan for future work.

2 APPROACH

As the biomedical field is a knowledge-based

discipline, Semantic Web technologies governing

knowledge representation are suitable to tackle

transparent search, request, manipulation, integration

and delivery of information (Baker, 2007). The

vision of data sharing in biomedical systems also

requires common standards for data storage and

novel frameworks for cross-referring terms and their

biological contexts, expressed as controlled

vocabularies or ontologies, between different types

of data (Nature, 2004). In this context, an ontology

is a formal specification of the shared

conceptualisation for a domain of interest, which

includes the definition of the types of objects

occurring in the domain, their attributes and

relations between them (Staab, 2004). Ontologies

are defined by means of a logical formalism

(Baader, 2004).

On the other hand, Grid computing refers to a

distributed computing infrastructure supporting

resource-sharing in wide-area networks for advanced

science and engineering (Foster, 2004). Grid

computing deals with system and syntactic

heterogeneity, i.e. it tackles the coexistence of

several hardware platforms and operating systems as

well as different protocols and encodings. This

infrastructure is suitable to support scientific

practice in biology (Goble, 2001), characterised by

distributed collaborations.

In this paper, we argue that combining a Grid

service infrastructure with biomedical ontologies

could prove beneficial to several aspects of cancer

registries’ activity. While Grid computing enables

collaboration in a distributed and heterogeneous

environment (in terms of software and hardware

platforms, protocols, etc.), ontologies enable a

common understanding of the domain knowledge by

providing a formal specification of the

conceptualisation shared by experts. The benefits

that these technologies can provide to cancer

registries are analysed in the following sections.

The approach of building common specifications

on the basis of a common semantic model is already

strongly encouraged, and proved successful, by

Semantic Web projects and Grid services and

technologies, as presented in the related work

section.

BIOMEDICAL ONTOLOGIES AND GRID COMPUTING - As New Resources for Cancer Registries

513

2.1 Ontologies and Cancer Registration

Rector et al. (Rector, 2006) analyse the relation

between coding systems and ontologies and

distinguish between 'information models' and

'meaning models', respectively. While 'information

models' specify data structures for healthcare records

and messages, 'meaning models' or 'ontologies'

specify human conceptualisations of reality. Thus,

'information models' are metamodels of the 'meaning

models' and are used to specify validity conditions

for data structures used by coding systems. On the

other hand, ontologies are used to test the accuracy

of the representation of the world. Consequently, if

we take 'disease' as an example, corresponding

individuals in the two models represent individual

illnesses (John's flu) and classes of illnesses

(conditions). This decoupling makes it possible to

reason separately about the two models, which are

about separate realities. Beside the trivial

observation that a code is not a condition or a

patient, in practice coding systems and the models

behind them are usually based on no or flawed

meaning models.

In the context of cancer registries, we advocate

to follow the same distinction between coding

systems and ontologies to obtain the following

benefits.

Firstly, the development of a specific ontology

for the domain of cancer registration would be an

accurate model, independent from information and

coding models and their original intended purposes.

It would also encourage the standardisation of

operational processes.

Secondly, the ontology would subsume all the

relevant notions agreed upon by data managers and

medical experts, in the light of the current

knowledge (concepts, relationships and restrictions).

Additionally, formal ontologies are specified in

suitable logic languages (typically in Description

Logic languages, which are a decidable fragment of

first order classical logic), by means of specialised

tools. This enables the use of automatic reasoners for

the computation of satisfiability of individual

instantiations of the concepts. Thus, validation rules

can be abstracted from the registry implementation,

to facilitate sharing and maintenance and rules

manipulation and updating would be accessible to

domain experts not necessarily versed in ICT.

Finally, an ontology developed on solid

theoretical principles, shared by the larger healthcare

and research community, would bridge the gap

between cancer registries and other repositories of

relevant data, such as tissue banks, clinical

administration systems, specialised registries for

related morbidities and screening databases among

the others, provided the ontology, the meaning

model, is wide enough. This could be a longer term

achievement, although newly conceived repositories,

such as tissue and imaging banks, may be more up to

date with Semantic Web technologies and ready to

share a semantic model.

2.2 Grid Computing and Cancer

Registration

While an ontology can bridge the semantic gap

between several resources, Grid computing enables

collaboration and resource-sharing by providing a

suitable middleware infrastructure. As regards

cancer registries, implementing a sound strategy

based on Grid services can facilitate data-sharing in

the epidemiology domain as well as providing other

potential advantages enumerated below.

Firstly, the integration with other cancer research

resources, as those mentioned above, is possible.

Current examples include projects like the cancer

Text Information Extraction System (caTIES),

whose ultimate goal is to integrate seamlessly

heterogeneous resources that provide annotations for

individual tissue samples. Cancer registries already

contain several items of information manually

extracted from clinical notes and reports, in the form

of cancer registrations, which can complement other

sources of clinical and pathological data for tissue

banks.

Secondly, it is possible to provide services that

can be used across cancer registries. For instance,

the core of the caTIES system is an Information

Extraction engine for pathology reports. Some of the

authors are exploring the possibility of customising

such a service for cancer registration purposes

(Napolitano, 2008). Additionally, these services can

be combined into workflows that can support the

business logic.

Last but not least, the requirement to operate

with Grid-compliant services will act as a stimulus

to develop the desiderata list mentioned in the

Approach section, in terms of accuracy and

standardisation. In particular, agreed information

and meaning models which can form a common

platform for the definition of shared, principled

cancer registration rules and mapping between

coding systems.

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3 MOTIVATING EXAMPLE

This section illustrates how the information and

meaning models (Rector, 2006) can be interfaced

and how to exploit the expressivity of formal

ontologies to automatically enrich the information

recorded in a Cancer Registry.

An example of a coding system created with

sound clinical motivations is provided by the TNM

staging system (UICC). Smaller tumours, tumours

confined to the primary site and not involving

regional lymph nodes or distant organs, commonly

indicate better prognosis for the patients. Based on

this observation, a TNM stage provides a staging

code as a combination of a T value (mainly based on

the size of the tumour), an N value (based on the

presence of metastasis in lymph nodes close to the

tumour site) and an M value (based on the presence

of distant metastasis: M0=no distant metastases,

M1=distant metastases present, MX=information not

available). Thus, is it possible to automatically infer

a TNM staging code for a patient’s disease, if this is

not explicitly provided by the pathologist?

3.1 Inferring a TNM Staging Code

using an OWL Ontology

Let us focus on the M value of the TNM stage. It is

assumed that the main Information System of a

model Cancer Registry is equipped with a

component projecting the incoming information onto

a formal ontology. Also, it is assumed that some

incoming records from a Patient Administration

Systems and Tissue Pathology Reporting Systems

cause the creation, in this additional knowledge

base, of the individuals john, john_Cancer and

john_Metastasis as instances of the classes Person,

Cancer and Metastasis respectively, together with

relevant relationships:

john has_disease john_Cance

(1)

john_Cancer has_metastasis

ohn

Metastasis

(2)

Finally, assume that the ontology also contains the

following axioms, embodying relevant knowledge of

this domain (in Manchester OWL syntax (Horridge,

2006)):

Patient ≡ Person AND has_disease

SOME Disease

(A1)

Cancer ⊆ Disease

(A2)

M1_Cancer ≡ Cancer AND

has

metastasis SOME Metastasis

(A3)

M1_Cancer ⊆ has_TNM_M SOME M1

(A4)

After classification by a reasoner, John is inferred to

be a Patient with an instance of an M1_Cancer. This

information can be easily extracted from the

semantic layer, by querying the inferred ontology,

and then, transferred to the registry database.

The key advantage of this approach derives from

the possibility to keep separate the semantic model,

represented by the first two axioms (A1-A2), from

the information model, represented by the last two

axioms (A3-A4). These information model axioms

provide the actual interface between the TNM

staging system and the meaning model (A1-A2),

which models the reality as described by scientists.

These models may reside in two distinct ontologies,

eventually combined as modules of the working

system. One will be ‘authoritative’ from a scientific

point of view, in the sense that the widest agreement

will be sought around it by the domain experts. The

other will be ‘ephemeral’, in the sense that it will be

subject to constant and more frequent revision, in the

light of the more dynamic (and more arbitrary)

nature of the coding systems.

4 RELATED WORK

This section presents related work using Grid and

Semantic Web technologies in the context of

biomedical systems. These systems follow a service-

oriented architecture and use semantic resources,

such as controlled vocabularies or ontologies, to

integrate heterogeneous and distributed data

resources.

The cancer Biomedical Informatics Grid

(caBIG™) (Fenstermacher, 2005) is a virtual

informatics infrastructure building a federated

environment to connect data, research tools,

scientists and organisations in the cancer research

community. It is an initiative of the National Cancer

Institute in the United States. The underlying Grid

middleware is called caGrid and it extends a basic

Grid infrastructure by focusing on data modelling

and semantics. caGrid adopts a model-driven

architecture requiring that all the data types are

formally described, curated and semantically

harmonised. In the United Kingdom, the National

Cancer Research Institute Informatics Initiative is

developing the Oncology Information eXchange

(ONIX), an informatics platform to facilitate access

to and movement of data generated from cancer

research. ONIX key requirement is to be

interoperable with caBIG™. Within this project,

some of the authors are working on the ONIX

BIOMEDICAL ONTOLOGIES AND GRID COMPUTING - As New Resources for Cancer Registries

515

Semantic Federated Query Infrastructure (González

Beltrán, 2008), which allows to perform queries over

distributed resources in terms of concepts from the

domain ontology. By following the approach

presented in this paper, cancer registries could also

exploit this additional functionality.

caBIG™ consists of several projects and

software tools. In particular, one tool that is relevant

for cancer registries is the cancer Information

Extraction System (caTIES), which is designed to

populate data structures with information extracted

and coded from surgical pathology reports. It uses

controlled terminologies and provides an interface

for querying, browsing and acquiring annotated data.

The main text process functionality is managed

using GATE (Cunningham, 2002), which is a

widely-used open-source natural language

processing framework.

DartGrid (Chen, 2006a-b-c) is a project that

started in 2002 for the Traditional Chinese Medicine

(TCM) community, aiming at integrating

heterogeneous and distributed legacy relational

databases. DartGrid builds an RDF-view of the

relational databases, even considering incomplete

information, and supports RDF queries over these

views.

The project Advancing Clinical-Genomic

Clinical Trials (ACGT) (Tsiknakis, 2006) also aims

at building a biomedical Grid for data resources in

Europe. ACGT supports data integration based on

ontological representation of clinical and

genomic/proteomic data taking into account standard

clinical genomic ontologies and metadata.

The use of OWL-DL reasoners for tumour

grading has already been investigated in (Dameron,

2006). In particular, some limitations of the OWL

language (modelling negative restrictions and

continuous numeric ranges among the others) are

discussed.

(Puleston, 2008) focuses on the balance of

distributing the representation of biomedical

knowledge and rules between a declarative

(ontological) model and a programmatical (software)

model. They conclude that different models are

appropriate to different tasks and actors in the

development of systems and applications in the

Health Care realm. This additional distinction is also

crucial and we believe it should be considered

carefully when implementing meaning and

information models in a production Information

System.

5 DISCUSSION AND FUTURE

WORK

Cancer registry information systems need to deal

with a variety of data annotated with several

different coding systems. The development of a

unique coding system is an elusive goal, as it is

unlikely that a single terminology could consider the

great range of functions needed. Thus, in the

scenario of many co-existing coding systems, issues

such as interpreting the ambiguous terms from

distinct coding systems, coping with different

versions of the terminologies and providing

mappings between codes must be considered.

In this paper, we have argued that the most

efficient solution to this problem for cancer

registries is to combine two cutting-edge

technologies such as Semantic Web and Grid

computing. Thus, we propose to build a semantic

layer on top of cancer registries sub-systems using a

myriad of coding systems to annotate data. This

semantic layer includes a domain ontology

providing a uniform view across sub-systems and it

is in charge of providing mappings to the used

terminology systems.

We have shown that the additional benefits of

using a logic-based representation of information for

cancer patients can be exploited to automate coding

procedures. This automation is based on the

interaction between the scientific knowledge,

embedded in the semantic layer, and the coding

conventions guiding the structuring of this

knowledge for specific purposes.

The projects revolving around the Semantic Web

and the Grid infrastructure show that the use of

formal ontologies is the preferred route to semantic

interoperability. In this paper, we have shown that

this approach in the extraction and coding of

pathological information for cancer patients has the

potential to produce immediate, beneficial effects.

As the next step, we will identify other aspects of

cancer registration that would benefit in the short

term from such a strategy. It is expected that

consequent positive results would generate a

'dragging effect', stimulating the extension of

research and applications to the remaining aspects of

cancer registry activities, where benefits seem to be

less obvious or less immediate. Thus, we claim that

the use of ontologies in conjunction with the

exploitation of relevant Grid services is a route that

should be pursued in cancer registration.

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ACKNOWLEDGEMENTS

G. Napolitano and C. Fox work in the Northern

Ireland Cancer Registry, funded by the Department

of Health, Social Services and Public Safety

Northern Ireland (DHSSPSNI). A. González Beltrán

and A. Finkelstein are grateful for the funding to

Cancer Research UK and the National Cancer

Research Institute Informatics Initiative.

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