Evaluation of the uncertainty in calculating nanodosimetric quantities due to the use of different interaction cross sections in Monte Carlo track structure codes.

Biological effects induced by diverse types of ionizing radiation are known to show important variations. Nanodosimetry is suitable for studying the link between these variations and the patterns of radiation interactions within nanometer-scale volumes, using experimental techniques complemented by Monte Carlo track structure (MCTS) simulations. However, predicted nanodosimetric quantities differ among MCTS codes, primarily because each code employs distinct molecular-scale particle interaction models. This multi-code study examines these variations for low-energy electrons (20-10,000 eV), which play a critical role in energy deposition and biological effects by virtually all types of ionizing radiation. Specifically, the hypothesis tested in this work is that inter-code variability in nanodosimetry results is mainly caused by differences in assumptions regarding total interaction cross sections. Ionization cluster size distributions and derived nanodosimetric parameters were simulated with seven MCTS codes (PARTRAC, PHITS-TS, MCwater, PTra, and three Geant4-DNA options) in liquid water as a surrogate for biological tissue. Significant inter-code differences were observed, especially at the lowest energies. They were substantially reduced upon replacing the original cross sections in each code with a common, averaged dataset, created ad-hoc for this study and not based on theoretical assumptions. For example, for 50 eV electrons in 8 nm spheres, the variability in the predicted mean ionization numbers decreased from 23% to 5%, and in the probability of inducing two or more ionizations from 34% to 7% (relative standard deviations). This quantification demonstrates that total interaction cross sections are the primary source of uncertainty at low electron energies. A sensitivity test using DNA damage simulations with the PARTRAC code revealed that cross section variations notably affect biological outcome predictions. Replacing the code's original cross sections with the averaged ones increased the predicted double-strand break yield by up to 15%. These findings underscore the urgent need for improved characterization of low-energy electron interaction cross sections to reduce uncertainties in MCTS simulations and enhance mechanistic understanding of radiation-induced biological effects.