JMIR Publications

ABSTRACT

Background:

Semantic interoperability in healthcare, essential for seamless integration of information systems, is partially achieved through the use of terminologies and common data standards that define the semantic structure of data. Various complexities arise using real-world healthcare data, including different interpretations of terms and concepts, as well as gaps in domain coverage in standard terminologies. However, ensuring compatibility becomes increasingly challenging when Big Data is distributed across diverse repositories that employ heterogeneous healthcare standards and overlapping terminologies. Ontologies are key solutions to bridge these gaps, enabling consistent semantic interoperability and data harmonization.

Objective:

We aim to develop and validate a hyper-ontology within the EUCAIM project to semantically integrate and harmonize clinical, biological, and imaging metadata, along with associated data from heterogeneous, disparate cancer image data models, to achieve semantic interoperability in oncology and medical imaging. The hyper-ontology will be employed to support several EUCAIM components, including the ETL process, federated query, image annotation and segmentation, and ultimately, AI federated processing.

Methods:

The ontology development process combines real-world data from a network of European projects on cancer imaging (AI4HI) with their semantic mappings, as well as conceptual unpacking and modeling of the mCODE specifications. This is supported by ontology grounding, layering, and modularization. We adopted this hybrid approach to simplify ontology design, semantically reflect oncology's essential entities and their interactions, and enhance the extensibility and reusability of the hyper-ontology. We initiated ontology development with a set of competency questions (CQs) derived from the provided knowledge, which helped clarify the ontology's scope and requirements and identify inconsistencies or incomplete information. We also assessed whether the requirements were fulfilled by formalizing the CQs using SPARQL.

Results:

We developed a FAIR-compliant hyper-ontology that semantically integrates and harmonizes clinical, biological, and imaging metadata and data spread across disparate sources. The ontology also captures and accurately represents the oncology and medical imaging domains. The hyper-ontology, which covers various cancer types, is rich in axiomatizations and mapping patterns, supporting the semantic understanding and harmonization of heterogeneous data. Additionally, semantic mappings are established across data models and standards, as well as with biomedical ontologies, terminologies, and standards, ensuring the efficient and meaningful sharing and integration of healthcare data. Finally, we demonstrated the applicability of the hyper-ontology using real-world prostate and breast cancer use cases.

Conclusions:

EUCAIM’s hyper-ontology is a valuable effort that provides a unifying framework for the essentials of oncology and medical imaging, facilitating communication among disparate and heterogeneous cancer image data models. The ontology model is evaluated and validated using multiple methods, demonstrating compliance with the specified ontological requirements. Challenges include ensuring that the ontology is scalable, extensible, and applicable, given the complexity and dynamic nature of the application domain.