Technical note: A knowledge graph approach to registering tumour specific data of patient‐candidates for proton therapy in the Netherlands. Issue 2 (20th December 2022)

• Record Type: Journal Article
• Authors: Kalendralis, Petros; Sloep, Matthijs; Choudhury, Ananya; Seyben, Lerau; Snel, Jasper; George, Nibin Moni; Veugen, Joeri; Veening, Martijn; Langendijk, Johannes A.; Dekker, Andre; van Soest, Johan; Fijten, Rianne
• Is Part Of: Medical physics. Volume 50:Issue 2(2023)
• Page Start: 1044
• Page End: 1050
• Publication Date: 2022-12-20
• Subjects: artificial intelligence -- FAIR -- knowledge graph -- proton therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.16105
• Languages: English
• ISSNs: 0094-2405

Abstract

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.