9. Conclusions

This deliverable presents the initial version of the EUCAIM CDM and hyper-ontology for data interoperability. In relation to the first deliverable (D5.1), this document provides a well-established analysis of the strategy developed to achieve the initial goals of the EUCAIM CDM and hyper-ontology. Publications submitted and accepted during the hyper-ontology development support the work accomplished.

Regarding the hyper-ontology development process, we encountered challenges in the knowledge acquisition phase (Section 4.3) to collect the standard clinical/biological and imaging data/metadata provided by the AI4HI projects. For the clinical knowledge, some data/metadata were customized depending on the projects’ resources, or standard code/vocabulary was lacking, which required an effort to associate this information with standard ontological/terminological resources. For imaging knowledge, the provided data/metadata was mainly DICOM tags and names used for image querying or segmentation, which is insufficient for a semantic representation of imaging knowledge in the hyper-ontology. Interestingly, the proposed approach (Section 4.4) has helped to overcome these challenges. First, the ORSD document was produced, which helped to organize all the collected data and metadata and classify them by cancer type and project, facilitating the detection of inconsistencies and lack of information. Second, the grounding of the hyper-ontology in mCODE has supported covering the essentials of the oncology domain, mainly for clinical aspects. For the imaging model, we relied on FHIR specifications around Imaging study and Series, and their relationships with Modality, Laterality, and other imaging aspects. Although the bottom-up strategy, which relies on the projects’ clinical and imaging knowledge, is crucial for developing the hyper-ontology as a domain and application-oriented ontology, the top-down has maintained the ontological model by grounding the hyper-ontology in the oncology domain. Also, the intervention of experts in revising and enriching the semantic content hyper-ontology has enhanced the generic content and expanded it by including clinically verified semantic patterns. Finally, the hyper-ontology is validated by:

1- efficiently and explicitly representing the provided use cases by populating the hyper-ontology semantic content, including the concepts and relations, based on the individuals (instances) harvested from these use cases (Section 7);

2- instantiating the EUCAIM-CDM to represent real-world use cases around prostate and breast cancers using the hyper-ontology concepts (Section 7);

3- applying SPARQL queries to request cancer patient information, such as lab tests, procedures, imaging and clinical results (Annex 1).

Interestingly, EUCAIM's hyper-ontology, a FAIR-compliant ontology model that effectively reflects oncology’s real-world entities, has supported a seamless integration with the EUCAIM CDM, a significant fulfillment for maintaining semantic interoperability in the context of the EUCAIM project.