JMIR Publications

ABSTRACT

Background:

Cancer research increasingly relies on heterogeneous datasets such as electronic health records (EHRs), genomic sequences, imaging, and multi-omics data. Robust research infrastructures are required to integrate these sources, yet existing approaches remain fragmented. Knowledge models, including ontologies, taxonomies, and knowledge graphs, provide standardized frameworks for data structuring, interoperability, and reuse, but their effectiveness in oncology research is not fully synthesized.

Objective:

This study aimed to systematically review knowledge models applied in cancer research infrastructures and to evaluate their role in addressing data heterogeneity, scalability, We conducted a systematic literature review following the guidelines of Kitchenham and Charters and the PRISMA 2020 framework. Searches were performed in PubMed, Scopus, Web of Science, and other digital libraries for studies published between 2015 and 2025. Inclusion criteria required that articles describe or evaluate knowledge models used in cancer data repositories. Three reviewers independently screened and assessed studies using predefined quality criteria. Data extraction covered model type, application domain, data types, methodological approaches, and reported challenges. We summarized the findings in a PRISMA flow diagram. and interoperability.

Methods:

We conducted a systematic literature review following the guidelines of Kitchenham and Charters and the PRISMA 2020 framework. Searches were performed in PubMed, Scopus, Web of Science, and other digital libraries for studies published between 2015 and 2025. Inclusion criteria required that articles describe or evaluate knowledge models used in cancer data repositories. Three reviewers independently screened and assessed studies using predefined quality criteria. Data extraction covered model type, application domain, data types, methodological approaches, and reported challenges. We summarized the findings in a PRISMA flow diagram.

Results:

A total of 39 primary studies were included. Most studies (over 75%) employed formal ontologies, particularly the National Cancer Institute Thesaurus (NCIT) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), to ensure semantic interoperability. Knowledge graphs, thesauri, and taxonomies were also reported. The models were applied to genomics, proteomics, clinical trials, imaging, and patient-reported outcomes. Key contributions included enhanced semantic data integration, ontology-based data structuring, and improved information retrieval and decision support. Reported challenges included scalability limitations in large-scale applications, manual curation demands, ontology evolution, and restricted usability for clinicians. Opportunities highlighted in the literature involved machine learning-assisted ontology enrichment, modular architecture, and community-driven standards such as the Open Biological and Biomedical Ontologies (OBO) Foundry.

Conclusions:

Knowledge models are central to enabling interoperable and reusable infrastructures in cancer research. Ontologies and knowledge graphs provide essential foundations for semantic integration across heterogeneous biomedical data. Despite advances, challenges remain in scalability, sustainability, and real-world adoption within clinical workflows. Future research should prioritize automation, user-centered tools, and cross-institutional collaboration to ensure that semantic technologies can transition from prototypes to operational infrastructures supporting precision oncology. Clinical Trial: Not applicable - this study is a systematic review and did not involve a clinical trial.