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 extensive curation.

Methods

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.

Results

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%

SUBMITTER: Doran SJ 

PROVIDER: S-EPMC10869673 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature