%0 Generic
%A Espinoza Bornscheuer, Ignacio Guillermo
%D 2013
%F heidok:15341
%R 10.11588/heidok.00015341
%T Computer simulation of the radiation response  of hypoxic tumours
%U https://archiv.ub.uni-heidelberg.de/volltextserver/15341/
%X In radiotherapy, it is important to predict the response of tumours to irradiation prior to the  treatment. This is especially important for hypoxic tumours, which are known to be highly radioresistant.  Mathematical modelling based on the dose distribution and biological input parameters  may help to improve this prediction and to optimize the treatment plan. In this work a radiobiological  model was developed, which simulates the growth and the response to radiotherapy of  tumours considering patient‐specific information of the tumour. The model is based on voxels,  containing tumour, capillary, normal and dead cells. Radiation‐induced killing of tumour cells is  calculated by the Linear‐Quadratic Model (extended for considering hypoxia). Proliferation and  resorption of cells are modelled by exponential laws. Computed tomography, positron emission  tomography and magnetic resonance imaging may be used for the initial characterisation of the  tumour. To simulate the response of hypoxic tumours properly, special emphasis was put on the  description of the intratumoural oxygenation. For this, a second complementary model was  developed to simulate the microscopic oxygen distribution, considering as input the vascular  fraction per voxel, which is in principle measurable non‐invasively in patients, and assuming  certain vascular architectures. The oxygen distribution is obtained by solving a reaction‐diffusion  equation using the Particle Strength Exchange method. Including the fractionation regime and the  planned dose distribution of the radiation treatment, the spatial‐temporal behaviour of the  tumour is simulated. The model describes the appearance of hypoxia during tumour growth and  the reoxygenation processes during radiotherapy. Among other parameters, the tumour control  probability can be calculated. The sensitivity of the tumour response on the values of different  parameters is systematically studied. The results are in accordance with published results. Prior to  clinical application, the model has to be further validated with experimental and clinical data.