Publishing linked and FAIR-compliant radiomics data in radiation oncology via ontologies and Semantic Web techniques

Medical images potentially embed much more information ('features') than can be exploited via visual inspection. Radiomics, the automated extraction of informative quantitative imaging features from patients' scans, could provide additional knowledge besides clinical prognostic factors for decision support systems in radiation oncology[1]. However, several limitations exist: no consensus on radiomics features' standardization, strong feature dependencies on how images are acquired and on settings (e.g. digital image pre-processing) defined for computations, poor quality of reporting and lack of transparency[2]. The IBSI (Image Biomarker Standardization Initiative) is a worldwide effort aiming at the standardization of radiomics computations[3]. One of the pillars of the IBSI workbook is that simply recording and comparing raw features values is not enough. Storing metadata associated with features computation, as well as the possibility to overcome differences in nomenclature between different computational packages to guarantee their interoperability and reproducibility in multi-center studies is needed. Also, radiomics data and metadata should be connected to corresponding clinical data (linked data) as input for AI algorithms. In this study, we present a proof-of-concept study using our newly developed radiomics ontology, combined with Semantic Web technologies, as instrument for enabling interoperability of radiomics data following FAIR principles: a) Findable? associated radiomics studies data and metadata have unique identifiers as per the Radiomics Ontology (RO); Accessible? metadata and data for a radiomics experiment are permanently stored in repository (e.g. SPARQL endpoint); Interoperable? via universal concepts defined in the RO full experiment results and methods can be retrieved; Reusable? data and metadata can be re-used to re-produce the study.