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

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^*

^*Kontaktforfatter

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

1 Citationer (Scopus)

19 Downloads (Pure)

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.

Originalsprog	Engelsk
Artikelnummer	100485
Tidsskrift	Physics and imaging in radiation oncology
Vol/bind	27
ISSN	2405-6316
DOI	https://doi.org/10.1016/j.phro.2023.100485
Status	Udgivet - jul. 2023

Bibliografisk note

Adgang til dokumentet

10.1016/j.phro.2023.100485Licens: CC BY-NC-ND 4.0

Refsgaard et al. (2023). End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohortForlagets udgivne version, 1,21 MBLicens: CC BY-NC-ND 4.0

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

Andre filer og links

Link to publication in Scopus

Citationsformater

Refsgaard, L., Skarsø, E. R., Ravkilde, T., Nissen, H. D., Olsen, M., Boye, K., Laursen, K. L., Bekke, S. N., Lorenzen, E. L., Brink, C., Thorsen, L. B. J., Offersen, B. V., & Korreman, S. S. (2023). End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort. Physics and imaging in radiation oncology, 27, Artikel 100485. https://doi.org/10.1016/j.phro.2023.100485

@article{a8c27dc1b7ab49c19d61b512bf03a2e1,

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

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",

author = "Lasse Refsgaard and Skars{\o}, {Emma Riis} and Thomas Ravkilde and Nissen, {Henrik Dahl} and Mikael Olsen and Kristian Boye and Laursen, {Kasper Lind} and Bekke, {Susanne N{\o}rring} and Lorenzen, {Ebbe Laugaard} and Carsten Brink and Thorsen, {Lise Bech Jellesmark} and Offersen, {Birgitte Vrou} and Korreman, {Stine Sofia}",

note = "{\textcopyright} 2023 Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.",

year = "2023",

month = jul,

doi = "10.1016/j.phro.2023.100485",

language = "English",

volume = "27",

journal = "Physics and imaging in radiation oncology",

issn = "2405-6316",

publisher = "Elsevier",

}

Refsgaard, L, Skarsø, ER, Ravkilde, T, Nissen, HD, Olsen, M, Boye, K, Laursen, KL, Bekke, SN, Lorenzen, EL, Brink, C, Thorsen, LBJ, Offersen, BV & Korreman, SS 2023, 'End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort', Physics and imaging in radiation oncology, bind 27, 100485. https://doi.org/10.1016/j.phro.2023.100485

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

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.

KW - Automation

KW - Big data

KW - Breast cancer

KW - DICOM

KW - Data collection

KW - Data science

KW - Radiotherapy

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 -

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

Abstract

Bibliografisk note

Adgang til dokumentet

AUB Link

Andre filer og links

Fingeraftryk

Citationsformater