首页|Institute of Cancer Research Reports Findings in Myeloma (Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data)
Institute of Cancer Research Reports Findings in Myeloma (Curation of myeloma observational study MALIMAR using XNAT: solving the challenges posed by real-world data)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Springer Nature
New research on Oncology - Myeloma is the subject of a report. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, "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 extensive curation." Financial supporters for this research include National Institute for Health and Care Research, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, Cancer Research UK. The news reporters obtained a quote from the research from the Institute of Cancer Research, "Curation involved image aggregation, pseudonymisation, allocation between project phases, data cleaning, upload to an XNAT repository visible from multiple sites, annotation, incorporation of machine learning research outputs and quality assurance using programmatic methods. A total of 796 whole-body MR imaging sessions from 462 subjects were curated. A major change in scan protocol part way through the retrospective window meant that approximately 30% of available imaging sessions had properties that differed significantly from the remainder of the data. Issues were found with a vendor-supplied clinical algorithm for 'composing' whole-body images from multiple imaging stations. Historic weaknesses in a digital video disk (DVD) research archive (already addressed by the mid-2010s) were highlighted by incomplete datasets, some of which could not be completely recovered. The final dataset contained 736 imaging sessions for 432 subjects. Software was written to clean and harmonise data. Implications for the subsequent machine learning activity are considered. 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 multi-national imaging projects."
LondonUnited KingdomEuropeCancerCyborgsEmerging TechnologiesHealth and MedicineHematologyMachine LearningMyelomaOncology
NETL
NSTL
Springer Nature
