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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Detaylı Bibliyografya
Asıl Yazarlar:	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
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	SpringerOpen 2024-02-01
Seri Bilgileri:	Insights into Imaging
Konular:	Data curation Data annotation Magnetic resonance imaging Myeloma
Online Erişim:	https://doi.org/10.1186/s13244-023-01591-7
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

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

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