| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Physics and Imaging in Radiation Oncology, Vol 27, Iss , Pp 100485- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2405-6316 |
| DOI: |
10.1016/j.phro.2023.100485 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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