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SU‐E‐J‐273: Simulation of the Radiation Response of Hypoxic Tumors
Author(s) -
Espinoza I,
Peschke P,
Karger C
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4888327
Subject(s) - voxel , radiation therapy , hypoxia (environmental) , radiation treatment planning , irradiation , computer science , nuclear medicine , biological system , biomedical engineering , medicine , chemistry , physics , biology , radiology , artificial intelligence , oxygen , organic chemistry , nuclear physics
Purpose: In radiotherapy, it is important to predict the response of tumour to irradiation prior to the treatment. Mathematical modelling of tumour control probability (TCP) based on the dose distribution, medical imaging and other biological information may help to improve this prediction and to optimize the treatment plan. The aim of this work is to develop an image based 3D multiscale radiobiological model, which describes the growth and the response to radiotherapy of hypoxic tumors. Methods: The computer model is based on voxels, containing tumour, normal (including capillary) and dead cells. Killing of tumour cells due to irradiation is calculated by the Linear Quadratic Model (extended for hypoxia), and the proliferation and resorption of cells are modelled by exponential laws. The initial shape of the tumours is taken from CT images and the initial vascular and cell density information from PET and/or MR images. Including the fractionation regime and the physical dose distribution of the radiation treatment, the model simulates the spatial‐temporal evolution of the tumor. Additionally, the dose distribution may be biologically optimized. Results: The model describes the appearance of hypoxia during tumour growth and the reoxygenation processes during radiotherapy. Among other parameters, the TCP is calculated for different dose distributions. The results are in accordance with published results. Conclusion: The simulation model may contribute to the understanding of the influence of biological parameters on tumor response during treatment, and specifically on TCP. It may be used to implement dose‐painting approaches. Experimental and clinical validation is needed. This study is supported by a grant from the Ministry of Education of Chile, Programa Mece Educacion Superior (2).