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

Lasse Refsgaard; Emma Riis Skarsø; Thomas Ravkilde; Henrik Dahl Nissen; Mikael Olsen; Kristian Boye; Kasper Lind Laursen; Susanne Nørring Bekke; Ebbe Laugaard Lorenzen; Carsten Brink; Lise Bech Jellesmark Thorsen; Birgitte Vrou Offersen; Stine Sofia Korreman

doi:10.1016/j.phro.2023.100485

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

Phys Imaging Radiat Oncol. 2023 Aug 25:27:100485. doi: 10.1016/j.phro.2023.100485. eCollection 2023 Jul.

Authors

Lasse Refsgaard^{1

2}, Emma Riis Skarsø^{3

2}, Thomas Ravkilde⁴, Henrik Dahl Nissen⁵, Mikael Olsen⁶, Kristian Boye⁷, Kasper Lind Laursen⁸, Susanne Nørring Bekke⁹, Ebbe Laugaard Lorenzen¹⁰, Carsten Brink¹⁰, Lise Bech Jellesmark Thorsen^{1

4}, Birgitte Vrou Offersen^{1

3

4}, Stine Sofia Korreman^{3

4

2}

Affiliations

¹ Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark.
² Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
³ Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
⁴ Department of Oncology, Aarhus University Hospital, Aarhus, Denmark.
⁵ Department of Oncology, Vejle Hospital, University Hospital of Southern Denmark, Denmark.
⁶ Department of Oncology, Zealand University Hospital, Department of Clinical Oncology and Palliative Care, Næstved, Denmark.
⁷ Department of Oncology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
⁸ Department of Medical Physics, Aalborg University Hospital, Aalborg, Denmark.
⁹ Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark.
¹⁰ Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark.

Abstract

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.

Keywords: Automation; Big data; Breast cancer; DICOM; Data collection; Data science; Radiotherapy.