Abstract
. The present work shows the first extensive validation of the
(GSM
). This mechanistic and probabilistic model is trained and tested over cell survival experiments conducted with two cell lines (H460 and H1437), three different types of radiation (protons, helium, and carbon ions), spanning a very broad LET range from1 keVμm-1up to more than300 keVμm-1. Currently, the existing mechanistic radiation biophysical models show some limitations in describing cell killing without the addition of ad hoc corrections, especially in the high-LET regime, where the overkill effect is observed.
. The experimental irradiation conditions have been accurately reproduced with Monte Carlo simulations using the GEANT4-based TOPAS computational toolkit. We show the main and unique features of GSM2, i.e. how it can predict the biological response by considering the full information on the stochasticity of radiation through the microdosimetric spectrum, which is supposed to be the best descriptor of radiation quality.
. Well-matching results for different biological endpoints with the natural presence of the overkill effect fully display the predictive power of GSM
.
. This study shows the complete generality and flexibility of GSM
and its ability to successfully predict the cell survival probability from very different particle radiation fields. Consequently, we demonstrate the dependence of the relative biological effectiveness on the whole microdosimetric spectrum, which fully includes the stochasticity inherently given by radiation-matter interaction.