=Paper=
{{Paper
|id=Vol-3905/short1
|storemode=property
|title=Domain ontology proposition for central nervous system tumors in pediatric
patients
|pdfUrl=https://ceur-ws.org/Vol-3905/short1.pdf
|volume=Vol-3905
|authors=Mariana C. Pereira,Mell A. Matsuda,Lucas N. Lanferdini,Marialva Sinigaglia,Sílvio C. Cazella
|dblpUrl=https://dblp.org/rec/conf/ontobras/PereiraMLSC24
}}
==Domain ontology proposition for central nervous system tumors in pediatric
patients==
https://ceur-ws.org/Vol-3905/short1.pdf
Domain ontology proposition for central nervous system
tumors in pediatric patients
Mariana C. Pereira1,2,3*, Mell A. Matsuda1,2, Lucas N. Lanferdini1,2, Marialva Sinigaglia1,3
and Sílvio C. Cazella2
1 Children's Cancer Institute, São Manoel Street, 850, 90620-110, Porto Alegre, RS, Brazil
2 Federal University of Health Sciences of Porto Alegre, Sarmento Leite Street, 245, 90050-170, Porto Alegre, RS, Brazil
3 National Science and Technology Institute for Children’s Cancer Biology and Pediatric Oncology – INCT BioOncoPed,
Brazil
Abstract
Ontologies have been used to model the representation of knowledge, favoring information retrieval.
In the health scenario, they can be an excellent alternative for solving gaps in information dispersion
and lack of interoperability. This summary presents an ongoing project that aims to propose a domain
ontology for the systemic visualization of oncopediatric patients with Central Nervous System tumors.
Clinical records, clinical protocols and therapeutic guidelines are being screened to enrich the
ontological model. The NeOn Methodology will guide the ontology construction processes, which will
be modeled in Protégé. It is hoped that the model will contribute to the expansion of ontologies aimed
at the pediatric public and the health sector.
Keywords
Central Nervous System, Health Information Interoperability, Ontology, Pediatric Oncology 1
1. Introduction
Childhood cancer represents only 3% of the population's total tumors [1], which creates the
scenario that the disease is little explored in large-scale scientific studies. However, in the
Brazilian pediatric scenario, cancer is the main cause of death in children and adolescents [1],
which indicates the need for a careful look at the demands of this population. As a result,
investments in pediatric oncology are increasing, which not only generates an increase in the
chances of a cure but also in the amount of data available for study.
However, offering comprehensive and continuous care is a complex task that requires the
collaboration of a multidisciplinary team, often geographically dispersed, as reference centers
are mostly located in capital cities. This generates a large number of clinical records that are also
scattered, which favors the lack of interoperability, standardization, and the difficulty of
integrating data and clinical monitoring [2]. In addition, many terminological and classification
systems were not developed for automation [3, 4].
In this context, ontologies emerge as a possible solution to the problems of interoperability,
standardization and data recovery. In line with this, Ordinance No. 2.073/2011 of the Brazilian
Ministry of Health encourages the use of common ontologies, terminologies and classifications
Proceedings of the 17th Seminar on Ontology Research in Brazil (ONTOBRAS 2024) and 8th Doctoral and Masters
Consortium on Ontologies (WTDO 2024), Vitória, Brazil, October 07-10, 2024
∗ Corresponding author.
marianacp@ufcspa.edu.br (M. C. Pereira); mell.matsuda@ufcspa.edu.br (M. A. Matsuda);
lucas.lanferdini@ufcspa.edu.br (L. N. Lanferdini); msinigaglia@ici.ong (M. Sinigaglia); silvioc@ufcspa.edu.br (S. C.
Cazella)
0000-0002-7938-8356 (M. C. Pereira); 0009-0005-0244-6852 (M. A. Matsuda); 0009-0005-3853-9794 (L. N.
Lanferdini); 0000-0002-8324-6860 (M. Sinigaglia); 0000-0003-2343-893X (S. C. Cazella)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
for the definition and representation of concepts related to health [5]. Therefore, this project is
based on the need to propose a domain ontology for the systemic monitoring and visualization
of oncopediatric patients.
This article describes ongoing research to achieve the ontological proposition. The most
relevant information is presented in five sections, including in Section 2 the theoretical
framework, in Section 3 the related works, in Section 4 the research method and in Section 5 the
final considerations. Due to the complexity of the topic and its incipient study, there was a special
focus on cases of Central Nervous System (CNS) tumors.
2. Theoretical frameworks
2.1 Interoperability of Health Information and Ontologies
Interoperability of health information is understood as the ability of systems to exchange
information and use it [6]. This exchange can be of a syntactic or semantic nature, and, in the
semantic context, data agreement allows for more agile and automatic processing by computers
[7]. Thus, interoperability and ontology complement each other, since the ontology acts as a
guide to establish the standards that guide how terms relate to each other, acting as a
terminological control that defines the language or set of terms used for queries in the domain
area [8].
Regarding the development of ontologies, this is an interdisciplinary activity. Due to the
ability to specify the semantics of terms in different domains and to establish semantic
relationships between concepts, ontologies have been widely used in the health area [8-12], with
a domain ontology being understood as being responsible for defining and characterizing the
scope in which tasks will be performed, differentiating from task ontologies, whose focus is on
solving specific problems [8].
2.2 The scenario of pediatric cancer and Central Nervous System tumors
Clinical oncology is the specialty dedicated to the study, planning, treatment prescription and
monitoring of cancer patients [13]. Pediatric oncology serves patients aged 0 to 19 years and is
related to the most diverse types of neoplasms, whether benign or malignant. In the scenario of
this study, malignant tumors (popularly called cancer) will be treated and characterized by the
disordered growth of cells [14].
Tumors that affect the child and adolescent population differ significantly from those that
affect adults and tend to be more aggressive, resulting in more intense treatments and greater
susceptibility to late effects [1]. In the case of Central Nervous System (CNS) tumors, they are the
most common group of solid tumors in children and adolescents, accounting for 20% of all
neoplasms in childhood and with a higher incidence in children aged one to four years [15].
Known as gliomas, astrocytomas, meningiomas, medulloblastomas, etc., the types of CNS
tumors have different anatomical regions, classification, staging and treatment, as they present
specific histological characteristics that will determine the pattern of dissemination of the
disease [15]. Therefore, standardizing criteria such as staging and extent of disease is essential
to allow comparison of performance between different national centers [16].
Opportunely, the increase in biomedical databases and the need for more sophisticated data
retrieval favor the use of ontological models in healthcare and, therefore, improving
terminologies, making them understandable to humans and processable by computers, has
become an essential task. However, there are still gaps in the pediatric oncology domain, both in
terms of health terminologies and the most appropriate models for building ontologies without
practical ambiguities. For example, "tumor" can refer to both a physical mass and the illness
process that the patient faces and, thus, there is the same word to characterize different entities,
as an entity is material - physical tumor with size and weight, and the other is a malignant disease
– with duration in time [17].
Therefore, it is crucial to consider the complexity of domain details and the existence of
ambiguities to propose ontologies that support semantic interoperability. Thus, when talking
about standardization, we also talk about the development of ontologies. The relationship
between pediatric oncology and ontologies will be presented below, in the explanation of related
work.
3. Related work
A scoping review was performed to identify relevant work within the domain. The review and
its details are registered in the Open Science Framework 2, with the works most aligned with the
proposal of this project being presented. Although the area of application of ontologies in health
is promising, the review revealed a scarcity of studies that specifically address the pediatric
oncology scenario.
Santos et. al [18] propose an automated data mining system that uses ontology to facilitate
access to analytical information about brain tumors by public health decision makers, especially
in decentralized health systems. Similarly, Feilmester et al. [19] present a method for analyzing
database attributes using a reference ontology to support the development of an information
system for therapeutic planning of brain tumors, evaluating the effectiveness of the proposed
method in comparison with traditional methods.
The paper of Stewart et al. [20] highlights the development of the YouCan Ontology
framework, which uses a customized ontology to represent knowledge from a Clinical Practice
Guideline and generate personalized self-management reports for pediatric patients in the post-
treatment monitoring phase. The YouCan Ontology illustrates the ontology's ability to model
medical knowledge and support shared decision-making between patients and healthcare
professionals.
In another context, but still exploring the potential of ontology to integrate health data, Hansi
et al. [21] propose an ontology-based semantic data integration framework for integrative
analysis. The authors argue that the ontological approach facilitates the integration of
heterogeneous data, in addition to improving the documentation and communication of
integration processes.
The Protégé tool [22] stands out as a consensus among the studies reviewed for the
development of ontologies. Various medical terminologies were utilized, including NCI
Thesaurus, which offers standardized codes for biomedical concepts; SNOMED-CT, which
provides organized medical terms and definitions for clinical documentation; ICD Codes, which
classify diseases and their causes; TNM Classification, a global standard for malignant tumor
staging; and UMLS, which integrates biomedical vocabularies to enhance system
interoperability. Pre-existing ontologies, such as Disease Ontology, Ontology for Biomedical
Investigations and Cell Line Ontology, were reused in some studies, indicating opportunities for
reuse and collaboration in the area. However, the review also revealed a gap in the detailed
description of the adopted ontology modeling methods.
4. Methods
The methods follow a structured set of methodological steps, the first of which is mapping
variables from clinical records of pediatric cancer patients, which includes the identification and
standardization of relevant clinical data, such as diagnoses, staging, and treatment of the disease.
2 Domain ontology for pediatric oncology patients: a scoping review protocol - https://osf.io/8a3ep/
An ontological dictionary is in the developing stage to ensure consistency and standardization of
information. The specification of terminological and conceptual relationships between domains
will be a step that will ensure that the ontology accurately represents the complex interactions
between different concepts. Finally, the validation of knowledge will occur through consultation
with domain experts, consolidated sources of scientific literature, and the most widely used
ontologies. The chosen methodology, NeOn, will be introduced in the next section).
4.1 Mapping data to ontology
Variable mapping was conducted using clinical records from an epidemiological study 3 and made
available by the Children's Cancer Institute (ICI). The clinical records, provided in blank format,
only allowed the detailed identification and categorization of entries without involving patient
information. Since no human data is involved, this study does not require consideration by the
Research Ethics Committee, being registered and approved by the Research Committee of the
Federal University of Health Sciences of Porto Alegre4 and by the ICI Research Projects
Committee5.
Data structuring involved the creation of categories and subcategories that reflected the
complexities and specificities of pediatric oncology cases. The main categories include
demographic data, clinical characteristics, tumor specificities, treatment, responses to treatment
and adverse effects, as well as psychosocial aspects and quality of life of patients. The
categorization and organization of data are being oriented to support future clinical,
epidemiological and public health analyses, aiming to provide a solid basis for the developed
ontology.
4.2 Ontology modeling Ontology modeling
This study is in the development phase, employing the NeOn methodology [23] to model the
initial structure of a CNS-focused ontology. NeOn provides nine flexible options to facilitate
ontology and ontological network creation. For this work, scenarios 1, 2, and 9 were selected
(Figure 1) which focus on the ontology lifecycle from conception to implementation. These
scenarios encompass the reuse and reengineering of non-ontological resources and the
localization of ontological assets, enabling the progression from draft to planned, conceptualized,
and formalized ontology [23]. Given that this ontology is initially designed to address the
terminological relationships in pediatric oncology, with a focus on health services and
professionals, foundations such as BFO were not used in its construction. This is also one of the
reasons why the aforementioned NeOn scenarios were chosen. Considering the main purpose of
modeling, scenarios 1, 2 and 9 are the most suitable for our current needs.
3 “Acompanhar para transformar: um olhar integrado para o câncer infantojuvenil a longo prazo, considerando
aspectos clínicos, psicológicos e sociais”, CAAE 52044221.8.1001.5327
4 Number 201/2022
5 Number 2022-0108.01
Figure 1: NeOn scenarios in use, prepared by the authors (2024).
Among the entities preliminarily mapped are the classification of tumors, diagnostic
procedures, types of tumors, treatments performed, signs and symptoms and the international
classification of the disease (Figure 2 and Figure 3). Entities related to the patient will also be
included, such as gender, age group, place of birth, city and treatment center, etc. The codes
presented in morphology and topography are the formal descriptions presented in the ICD-O
[24] which allows the coding of all neoplasms by topography (site of disease), histology
(morphology/type of disease) and biological behavior (disease staging.
Figure 2: Entities modeling in Protégé, prepared by the authors (2024).
Figure 3: Preliminary modeling in Protégé, prepared by the authors (2024).
5. Final considerations
Regarding the study domain, ontology modeling could benefit clinical assistance, the
development of scientific research and health education, however, ontology proposals for this
domain are an incipient topic. Furthermore, the need to reduce the ontology domain for tumors
of the CNS also presents itself as a limitation. The present work is in the final phase, and this
article presented the proposal for a preliminary ontology modeling which aims to fill the gap
highlighted. Considering that the ontological model has not been finalized, it is expected, in the
next steps, that the ontology will be named, encompassing the scenario of pediatrics and central
nervous system tumors. Also, it is expected that the proposed model can significantly contribute
to the organization, mapping, understanding and interoperability of CNS tumor data for pediatric
patients. The use of BFO is considered for future projects aimed at the computer science area.
Acknowledgments
State of Rio Grande do Sul Research Support Foundation, FAPERGS Project 22/2551-0000390-7
(RITE CIARS), Children's Cancer Institute and Ronald McDonald Institute, grant number
2022062.
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