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 journalArticleAcademicpeer-review

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 pages7
JournalMedical Physics
Volume50
Issue number2
Early online date9 Dec 2022
DOIs
Publication statusPublished - Feb 2023

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