DNA double-strand breaks (DSBs) may result in cellular mutations, apoptosis, and cell death, making them critical determinants of cellular survival and functionality, as well as major mechanisms underlying cell death. The success of nanodosimetry lies in the reduction in the number of modeling parameters to be adjusted for the model to predict experimental data on radiation biology. Based on this background, this study modified and simplified the logistic nanodosimetry model (LNDM) based on radiation-induced DSB probability. The probability distribution of ionization cluster size \(P(\nu \vert Q)\) under irradiation with carbon-ion beams was obtained through a track-structure Monte Carlo (MC) simulation, and then, the nanodosimetric quantities and DSB probability were calculated. Combining the assumptions of the linear quadratic (LQ) model and LNDM, DSB probability-based modification and simplification of the LNDM were conducted. Additionally, based on the radiobiological experimental data of human salivary gland (HSG), Chinese hamster lung (V79), and Chinese hamster ovary (CHO-K1) cells, the least-squares method was used to optimize the parameters of the modified LNDM (mLNDM). The mLNDM accurately reproduced the experimental data of HSG, V79, and CHO-K1 cells, and the results showed that the model parameters r and \(m_{0}\) were independent of the cell type, that is, the biological effects of cells with different radiosensitivities can be characterized by adjusting only the model parameters k and \(P_{s\rightarrow l}\) . Compared with HSG and CHO-K1 cells, V79 cells had smaller k and \(P_{s\rightarrow l}\) values, indicating that that DSBs have a lower probability of eventually causing lethal damage, and sublethal events are less likely to interact to form lethal events, thereby having radioresistant characteristics. Compared with the LNDM, the mLNDM eliminates the tedious derivation process and connects the quantities characterizing radiation quality at the nanoscale level using radiation biological effects in a more direct and easy-to-understand manner, thus providing a simpler and more accurate method for calculating relative biological effectiveness for ion-beam treatment planning.
