TPL_ADMIT_JUMPCONTENT

Project Area D

Monte Carlo Simulations to Describe the Radiation Effect of Carbon Ions and Protons at the Cellular Level

Particle beams exhibit an elevated relative biological effectiveness (RBE) in comparison to photon beams. This implies that, for a given physical dose, the biological effect of particle beams is greater than that of photon beams. Conseuently, a lower physical dose is sufficient to kill the tumor cells. The calculation of the RBE is highly complex. For carbon ions, models such as the local effect model (LEM) and the microdosimetric-kinetic model (MKM) are used to model the RBE as a function of the radiation sensitivity of different tissue types. However, the uncertainties in the calculation of the RBE using these models are considerable, with deviations of up to 30%. Such models have not yet been clinically established for proton beams, although there are indications that protons may also have an increased RBE. It is therefore important to improve the modeling of the RBE in order to minimize dosimetric uncertainties in irradiation and to enable more precise predictions about the effect at the cellular level. Monte Carlo simulations are a very efficient method to characterize the effect of ionizing radiation at the cellular level.

The aim of the project is to further optimize and experimentally validate the description of the highly complex chemical and biological effects in the Monte Carlo code Geant4-DNA. This will facilitate the generation of clinically robust predictions of the relative biological effectiveness (RBE) for protons and carbon ions. With regard to DNA damage, it is necessary to ascertain whether the number and distribution of DNA damage induced by ionizing particle radiation and its complexity are accurately represented in the Monte Carlo code. The outcome is a better understanding of the effectiveness of ionizing particle radiation at the cellular level. This is essential for an exact modeling of the biological effectiveness, thereby enabling a more precise prediction of the dose distribution in cancer patient irradiation.

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