TY - JOUR
T1 - End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort
AU - Refsgaard, Lasse
AU - Skarsø, Emma Riis
AU - Ravkilde, Thomas
AU - Nissen, Henrik Dahl
AU - Olsen, Mikael
AU - Boye, Kristian
AU - Laursen, Kasper Lind
AU - Bekke, Susanne Nørring
AU - Lorenzen, Ebbe Laugaard
AU - Brink, Carsten
AU - Thorsen, Lise Bech Jellesmark
AU - Offersen, Birgitte Vrou
AU - Korreman, Stine Sofia
N1 - © 2023 Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.
PY - 2023/7
Y1 - 2023/7
N2 - 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 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 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.
AB - 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 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 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.
UR - http://www.scopus.com/inward/record.url?scp=85169845387&partnerID=8YFLogxK
U2 - 10.1016/j.phro.2023.100485
DO - 10.1016/j.phro.2023.100485
M3 - Journal article
C2 - 37705727
SN - 2405-6316
VL - 27
JO - Physics and imaging in radiation oncology
JF - Physics and imaging in radiation oncology
M1 - 100485
ER -