Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT).However, the tools for multi-centre data collection, curation and standardisation are not readily available.Automated batch DICOM export whirlwind direct2 solutions were demonstrated for a multicentre setup.A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data.
The setup was demonstrated in the DBCG pentair hose RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.