A DSB-Weighted Modification to the Mkm for Carbon-Ion Rbe
Abstract
Purpose
In the microdosimetric kinetic model (MKM), the quadratic coefficient β is commonly assumed to be independent of linear energy transfer (LET). However, experimental data suggest that β decreases at very high LET, likely because the conventional MKM does not explicitly account for LET-dependent differences in DNA double-strand break (DSB) induction. This study aims to incorporate a DSB-yield–ratio–based correction to re-estimate β and improve the accuracy of relative biological effectiveness (RBE) prediction.
Methods
Based on the microdosimetric correspondence between potentially lethal lesions (PLLs) and DNA DSBs in the MKM, this study theoretically derives that the β is proportional to the square of the DSB yield ratio between the investigated radiation and a reference radiation. Clonogenic survival data and γ-H2AX foci measurements of A172 human glioblastoma cells irradiated with carbon ions were obtained from the RadPhysBio database. MCDS was employed to simulate initial DSB yields induced by carbon ions and reference X-rays under identical irradiation conditions. The resulting DSB yield ratio was used to recalculate β for quantitative prediction of carbon-ion RBE.
Results
At DNA damage level, the simulated initial DSB yield was 26.5 DSB·cell-1·Gy-1, while the corresponding γ-H2AX foci measurements yielded 27.3 ± 1.4 foci·cell-1·Gy-1, showing agreement within experimental uncertainty. Compared with the conventional MKM using a fixed β0 = 0.05, which resulted in χ² = 0.041, the recalculated β parameter based on the DSB ratio (βC = 0.253) , reducing χ² to 0.002. The recalculated RBE10 was 2.696, in close agreement with the experimental value of 2.7, corresponding to a relative error of 0.15%.
Conclusion
This study enhances the predictive accuracy of the MKM for cell survival and RBE under carbon ion irradiation conditions by establishing the connection between the β parameter and DNA damage, providing an interpretable improvement method for modeling the biological effects of high-LET radiation.