Objective
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.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
Call for proposal
ERC-2013-StG
See other projects for this call
Funding Scheme
ERC-SG - ERC Starting GrantHost institution
72074 Tuebingen
Germany