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Biologically individualized, model-based radiotherapy on the basis of multi-parametric molecular tumour profiling
(bio-iRT)
Date du début: 1 janv. 2014,
Date de fin: 31 déc. 2018
PROJET
TERMINÉ
High precision radiotherapy (RT) allows extremely flexible tumour treatments achieving highly conformal radiation doses while sparing surrounding organs at risk. Nevertheless, failure rates of up to 50% are reported for head and neck cancer (HNC) due to radiation resistance induced by pathophysiologic factors such as hypoxia and other clinical factors as HPV-status, stage and tumour volume.This project aims at developing a multi-parametric model for individualized RT (iRT) dose prescriptions in HNC based on biological markers and functional PET/MR imaging. This project goes far beyond current research standards and clinical practice as it aims for establishing hypoxia PET and f-MRI as well as biological markers in HNC as a role model for a novel concept from anatomy-based to biologically iRT.During this project, a multi-parametric model will be developed on a preclinical basis that combines biological markers such as different oncogenes and hypoxia gene classifier with functional PET/MR imaging, such as FMISO PET in combination with different f-MRI techniques, like DW-, DCE- and BOLD-MRI in addition to MR spectroscopy. The ultimate goal of this project is a multi-parametric model to predict therapy outcome and guide iRT.In a second part, a clinical study will be carried out to validate the preclinical model in patients. Based on the most informative radiobiological and imaging parameters as identified during the pre-clinical phase, biological markers and advanced PET/MR imaging will be evaluated in terms of their potential for iRT dose prescription.Successful development of a model for biologically iRT prescription on the basis of multi-parametric molecular profiling would provide a unique basis for personalized cancer treatment. A validated multi-parametric model for RT outcome would represent a paradigm shift from anatomy-based to biologically iRT concepts with the ultimate goal of improving cancer cure rates.
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