Technical note: A knowledge graph approach to registering tumour specific data of patient-candidates for proton therapy in the Netherlands

Petros Kalendralis, Matthijs Sloep, Ananya Choudhury, Lerau Seyben, Jasper Snel, Nibin Moni George, Joeri Veugen, Martijn Veening, Johannes A. Langendijk, Andre Dekker, Johan van Soest, Rianne Fijten*

*Corresponding author for this work

    Research output: Contribution to journalCase note

    2 Citations (Scopus)
    101 Downloads (Pure)

    Abstract

    The registration of multi-source radiation oncology data is a time-consuming and labour-intensive procedure. The standardisation of data collection offers the possibility for the acquisition of quality data for research and clinical purposes. With this study, we present an overview of the different tumour group data lists in the Dutch national proton therapy registry. Furthermore, as a representative example of the workings of these different tumour-specific knowledge graphs, we present the FAIR (Findable, Accessible, Interoperable, Reusable) data principles-compliant knowledge graph approach describing the head and neck tumour variables using radiotherapy domain ontologies and semantic web technologies. Our goal is to provide the radiotherapy community with a flexible and interoperable data model for data exchange between centres. We highlight data variables that are needed for models used in the model-based approach (MBA), which ensures a fair selection of patients that will benefit most from proton therapy.

    Original languageEnglish
    Pages (from-to)1044-1050
    Number of pages6
    JournalMedical Physics
    Volume50
    Issue number2
    Early online date9-Dec-2022
    DOIs
    Publication statusPublished - Feb-2023

    Keywords

    • artificial intelligence
    • FAIR
    • knowledge graph
    • proton therapy

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