Over the past few years, more than 50 sub-projects have been created in the RACOON network- a strong indication of how lively, adaptable and thematically broad the joint infrastructure has become. The projects illustrate just how versatile RACOON is: The spectrum ranges from infectiological disease patterns to chronic inflammation, emergency medicine and oncology, from retrospective population studies to prospective comparative studies. The following selection shows a small selection of this diversity.
One of the early and formative projects is RACOON-COMBINE, which investigates quantitative imaging markers across the broad organ spectrum of COVID-19. By integrating paediatric, neuro- and cardiovascular imaging as well as AI-supported prognostic models, COMBINE has set methodological standards that now serve as the basis for many other projects.
Another focus of the network is on tumour diseases. With RACOON-PDAC, pancreatic carcinoma is moving centre stage. Image-based and clinical biomarkers are being developed on the basis of extensive multi-centre data in order to predict the response to therapy more precisely. In addition, PRECISE-MD networks around 20 sites in order to analyse pancreatic and liver tumours in a multidisciplinary manner and combine radiological, oncological and pathological information in joint AI models.
Paediatric oncology is also strongly represented: RACOON-RESCUE is investigating rare non-Hodgkin's lymphomas in children, drawing on registry data and using structured reporting and AI-based segmentation to improve staging and develop more reliable prognostic models in the long term.
With RACOON-FADEN, the network is focusing on a gynaecological topic - the early detection of Adenomyosis, an early form of endometriosis. MRI data collected prospectively and jointly with NUKLEUS and AI-supported image analyses are intended to identify new quantitative diagnostic features and improve diagnosis.
In contrast, RACOON-CORE-PE is investigating acute pulmonary embolism. The focus is on the question of how AI-based image analysis can support risk assessment and clinical decision-making in these emergencies.
Overall, it is clear that the growing number of over 50 sub-projects is far more than just quantitative growth. It shows how extensively RACOON is used as a research basis and how strongly the network is driving the further development of radiological research. With each new issue, RACOON becomes broader, more interdisciplinary and at the same time more clinically relevant - and is increasingly establishing itself as the central platform for imaging research of the future.