Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data.
Simon J DoranTheo BarfootLinda WedlakeJessica M WinfieldJames PettsBen GlockerXingfeng LiMartin LeachMartin KaiserTara D BarwickAristeidis ChaidosLaura SatchwellNeil SonejiKhalil ElgendyAlexander SheekaKathryn WallittDow-Mu KohChristina MessiouAndrea G RockallPublished in: Insights into imaging (2024)
• Heterogeneous data in the MALIMAR study required the development of novel curation strategies. • Correction of multiple problems affecting the real-world data was successful, but implications for machine learning are still being evaluated. • Modern image repositories have rich application programming interfaces enabling data enrichment and programmatic QA, making them much more than simple "image marts".