DICOM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project.

PRIMAGE is a European Commission-financed project dealing with medical imaging and artificial intelligence aiming to create an imaging biobank in oncology. The project includes a task dedicated to the interoperability between imaging and standard biobanks. We aim at linking Digital imaging and Communications in Medicine (DICOM) metadata to the Minimum Information About BIobank data Sharing (MIABIS) standard of biobanking. A very first integration model based on the fusion of the two existing standards, MIABIS and DICOM, has been developed. The fundamental method was that of expanding the MIABIS core to the imaging field, adding DICOM metadata derived from CT scans of 18 paediatric patients with neuroblastoma. The model was developed with the relational database management system Structured Query Language. The integration data model has been built as an Entity Relationship Diagram, commonly used to organise data within databases. Five additional entities have been linked to the "Image Collection" subcategory in order to include the imaging metadata more specific to the particular type of data: Body Part Examined, Modality Information, Dataset Type, Image Analysis, and Registration Parameters. The model is a starting point for the expansion of MIABIS with further DICOM metadata, enabling the inclusion of imaging data in biorepositories.

Artificial intelligence, radiomics and other horizons in body composition assessment.

This paper offers a brief overview of common non-invasive techniques for body composition assessment methods, and of the way images extracted by these methods can be processed with artificial intelligence (AI) and radiomic analysis. These new techniques are becoming more and more appealing in the field of health care, thanks to their ability to treat and process a huge amount of data, suggest new correlations between extracted imaging biomarkers and traits of several diseases as well as lead to the possibility to realise an increasingly personalized medicine. The idea is to suggest the use of AI applications and radiomic analysis to search for features that may be extracted from medical images [computed tomography (CT) and magnetic resonance imaging (MRI)], and that may turn out to be good predictors of metabolic disorder diseases and cancer. This could lead to patient-specific treatments and management of several diseases linked with excessive body fat.