In recent years, medicine has witnessed remarkable advancements that have revolutionized the treatment of various diseases, significantly improving prognosis. The evolution towards more personalized precision medicine is evident across various medical disciplines. In this context, radiology has evolved as an integral interdisciplinary specialty enabling accurate diagnosis, follow-up and clinical decision making. Despite its crucial role, conventional imaging methods and established workflows in radiology insufficiently adress rising examination volumes, expanding data, and growing demands for image interpretation within complex clinical contexts. Numerous studies have elucidated different technologies to amend those challenges within recent years, yet a significant translational gap remains.
The aim of the group is to investigate innovative translational technologies in radiology encompassing scanner-driven approaches such as spectral CT, as well as artificial intelligence applications such as large language models or computer vision-based AI tools, with the clear scope of enhancing validation and thereby clinical applicability. A special focus is set on applications in oncologic imaging.
- Spectral imaging in malignant melanoma
- Imaging biomarkers for patients with immune checkpoint inhibition
- Multiparametric spectral data for liver lesion characterization
- Large language models: Use cases in oncologic imaging workflows
- Center for Integrated Oncology (Paul Bröckelmann, Moritz Gräf, Barbara Eichhorst, Thomas Zander, Matthias Scheffler)
- Data Science Group, Institute for Diagnostic and Interventional Radiology (Liliana Caldeira, Mirjam Schöneck)
- Institute for Biomedical Informatics Cologne (Julia Gehrmann, Oya Beyan)