2026

 

 

Elian Rose

 

Medizinische Universität Innsbruck (Universitätsklinik für Radiologie)

 

The project is funded by the Jubiläumsfonds der Universität Innsbruck

 

 

Primary brain tumors remain among the most challenging oncologic diseases, with high morbidity and limited prognosis. Current standard therapy for a newly diagnosed GBM consists of multimodal therapy with surgical biopsy or resection, routinely followed by chemotherapy with temozolomide and radiotherapy. Post-treatment imaging plays a crucial role in assessing therapy response and guiding further management. However, differentiating true tumor progression from therapy-related effects, such as pseudoprogression or radiation necrosis, has shown to be very difficult based on conventional Magnetic Resonance Imaging (MRI). Contrast Clearance Analysis (CCA) has emerged as a promising imaging approach to address this challenge. The underlying priciple is that active tumor tissue is characterised by effective clearance of contrast agent, whereas in necrotic tissue the contrast agent accumulates over time. While several studies have demonstrated the potential of CCA to improve the differentiation between tumor progression and treatment-related effects, its integration into clinical workflows remains limited. This project aims in developing and scientifically validating an open-source, modular image analysis pipeline for CCA. We aim to provide an analysis pipeline designed to generate detailed, quantitative outputs on a voxel-wise level. The method will be evaluated using existing clinical data from the Medical University of Innsbruck in order to better support therapy decisions in the future.