Research powered by XNAT:MALIMAR
XNAT is an advanced management system for research images. It uses multiple technologies to provide researchers with the tools they need to process data and view data. The ICR has been at the forefront of developing new capabilities for XNAT, in particular, the ICR-XNAT-OHIF viewer (see Doran et al. Tomography 8.1 (2022). The XNAT Team has the privilege of working with outstanding scientists both locally and internationally, and our tools help them to deliver high-impact clinical studies. This page describes collaboration between the Royal Marsden, Institute of Cancer Research, Imperial College NHS Trust and Imperial College London on the project codenamed MALIMAR.
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, Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data, in press Insights into Imaging
FEATURED PUBLICATION: MALIMAR study
MAchine Learning In MyelomA Response (MALIMAR) is an observational clinical study combining “real world” and clinical trial data, both retrospective and prospective. 796 whole-body MR imaging sessions from 462 subjects, acquired on three MRI scanners over a ten-year window at two institutions, were curated. Specialised “XNAT-enabled” software was written to clean and harmonise data and the results used in machine learning pipelines
MALIMAR exemplifies the vital role that curation plays in machine learning studies that use real-world data. A research repository such as XNAT facilitates day-to-day management, ensures robustness and consistency, and enhances the value of the final dataset. The types of process described here will be vital for future large-scale multi-institutional and multinational imaging projects.
