Abstract
BackgroundRegional and epidemiological cancer registries are important for cancer research and the quality management of cancer treatment. Many technological solutions are available to collect and analyse data for cancer registries nowadays. However, the lack of a well-defined common semantic model is a problem when user-defined analyses and data linking to external resources are required. The objectives of this study are: (1) design of a semantic model for local cancer registries; (2) development of a semantically-enabled cancer registry based on this model; and (3) semantic exploitation of the cancer registry for analysing and visualising disease courses.ResultsOur proposal is based on our previous results and experience working with semantic technologies. Data stored in a cancer registry database were transformed into RDF employing a process driven by OWL ontologies. The semantic representation of the data was then processed to extract semantic patient profiles, which were exploited by means of SPARQL queries to identify groups of similar patients and to analyse the disease timelines of patients.Based on the requirements analysis, we have produced a draft of an ontology that models the semantics of a local cancer registry in a pragmatic extensible way. We have implemented a Semantic Web platform that allows transforming and storing data from cancer registries in RDF. This platform also permits users to formulate incremental user-defined queries through a graphical user interface. The query results can be displayed in several customisable ways. The complex disease timelines of individual patients can be clearly represented. Different events, e.g. different therapies and disease courses, are presented according to their temporal and causal relations.ConclusionThe presented platform is an example of the parallel development of ontologies and applications that take advantage of semantic web technologies in the medical field. The semantic structure of the representation renders it easy to analyse key figures of the patients and their evolution at different granularity levels.
Highlights
Cancer registries are an important part of the health information systems in local and regional health organizations
An ontology modeling the semantics of an institutional cancer registry has been developed
This ontology has driven the transformation of the simulated dataset into Resource description framework (RDF) and its storage in the semantic data store
Summary
Cancer registries are an important part of the health information systems in local and regional health organizations. Regional and epidemiological cancer registries are the foundation for cancer research and the quality management of cancer treatment. Regional cancer registries collect information about diagnosis, therapies and course of the disease [3], the most important being the histopathology of the primary tumor, including tumor staging and grading. Regional and epidemiological cancer registries are important for cancer research and the quality management of cancer treatment. A standardised common dataset has been developed to better support exhaustive data exchange with the epidemiological cancer registries, proposing the classification of diagnostic and treatment information with clinical coding systems. The most important clinical classification system applied in cancer registries is the International Classification of Diseases version 10 (ICD-10) [17] This classification system is divided in chapters, with blocks of diseases. SNOMED CT [18] has adopted ICD-O codes for the classification of tumor morphology
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