Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data

Abstract Objectives MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining “real-world” and clinical trial data, both retrospective and prospective. Images were acquired on three MRI scanners over a 10-year window at two institutions, leading to a need for extens...

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主要な著者:	Simon J. Doran, Theo Barfoot, Linda Wedlake, Jessica M. Winfield, James Petts, Ben Glocker, Xingfeng Li, Martin Leach, Martin Kaiser, Tara D. Barwick, Aristeidis Chaidos, Laura Satchwell, Neil Soneji, Khalil Elgendy, Alexander Sheeka, Kathryn Wallitt, Dow-Mu Koh, Christina Messiou, Andrea Rockall
フォーマット:	Artigo
言語:	Inglês
出版事項:	SpringerOpen 2024-02-01
シリーズ:	Insights into Imaging
主題:	Data curation Data annotation Magnetic resonance imaging Myeloma
オンライン･アクセス:	https://doi.org/10.1186/s13244-023-01591-7
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