Prediction of radiotherapy outcome using radiobiological modelling and positron emission tomography

Abstract:  Identification of cancer patients at high risk of relapse is very important with respect to choice of treatment. For instance, high-risk patients may be given higher radiation dose to the tumor, more chemotherapy, or preferably a treatment tailored according the individual's risk profile. The talk addresses preliminary results from the development of a mathematical radiobiological model to predict patient outcomes after radiotherapy of head and neck cancer. In the model, it is assumed that the density of clonogenic cancer cells is proportional to the uptake of a glucose-analog used in tumor imaging by positron emission tomography (PET). Moreover, it is known that cancer cells infected by the human papilloma virus (HPV) are more sensitive to radiation, and this is tentatively taken into account in the modelling. The presentation will show findings based on a cohort of 225 patients with cancer in the head and neck region that have undergone a PET examination prior to radiotherapy.