End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort

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 DICO...

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Detalles Bibliográficos
Autores principales: Refsgaard, Lasse, Skarsø, Emma Riis, Ravkilde, Thomas, Nissen, Henrik Dahl, Olsen, Mikael, Boye, Kristian, Laursen, Kasper Lind, Bekke, Susanne Nørring, Lorenzen, Ebbe Laugaard, Brink, Carsten, Thorsen, Lise Bech Jellesmark, Offersen, Birgitte Vrou, Korreman, Stine Sofia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Technical Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495662/
https://www.ncbi.nlm.nih.gov/pubmed/37705727
http://dx.doi.org/10.1016/j.phro.2023.100485
Descripción
Sumario: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.

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