Integration and Optimization of XNAT-Based Platforms for the Management of Heterogeneous and Multicenter Data in Biomedical Research

The rise of data-driven analysis methods in biomedical research has led to the need for proper data management. Organizing large datasets of heterogeneous biomedical data can be challenging, especially in multicentric studies, with the need to ensure data integrity, quality, and privacy compliance with laws. In this work, we report and discuss two solutions that we are starting to implement: a platform for collecting Computed Tomography imaging data of phantoms and associated metadata in a multi-centric study focused on radiomics, and a platform for gathering, sharing, and analyzing diverse data acquired in a project focused on FLASH radiotherapy. Both platforms will be built on top of the XNAT technology. Our goal is to establish a secure and collaborative medical research environment that promotes data sharing, customized workflow analysis, and stores data and results for subsequent studies. The key innovation is the creation of a personalized platform system that currently does not exist. This is essential from a scientific point of view to enable advanced statistical analysis and reveal non-trivial relationships among heterogeneous data. This cannot be achieved with disorganized data collection. The platforms will also integrate analysis tools and quality control pipelines executable directly from the platform on stored data.