A refined approach to compute nanodosimetric quantities for proton and ion radiotherapy treatment planning

<i>Objective</i>. To present a refined approach to calculate a database of nanodosimetric quantities and efficiently and accurately compute voxel-averaged ionization detail (ID) quantities for particle radiotherapy treatment planning (RTP) applications.<i>Approach</i>. Monte Carlo track structure was employed to compute nanodosimetric quantities at nanoscale. The new approach includes the extension of particle energy ranges to encompass clinically relevant values, consideration of isotopes, optimally spaced energies, and consistent treatment of secondary particles at nano and macroscale through application of common production cutoffs to avoid double-counting/omission of ionizations. At macroscale using Monte Carlo condensed history simulations, we devised a scoring approach to account for ID quantity changes across Monte Carlo steps with each step divided into substeps chosen to be consistent with database energies. The database calculated with the refined approach was compared to our previously published approach and we studied its impact on the identification of preferred ID quantities for their closest association with cell survival in three biological datasets.<i>Results</i>. The optimized energy binning reduced interpolation errors to below 1%. Across different isotopes, the ID quantities differed on average by 2% relative to that of the corresponding stable ion, resulting in an error of less than 0.3% when using a single ID value per atomic number in macroscopic calculations. Our refined approach revealed differences from our previous method resulting in increases of up to 20% and 10% in cluster dose SOBP's for carbon and oxygen ions, three-fold for protons. In addition, application of the refined approach altered the selection of the preferred ID quantity in one of the three datasets studied, highlighting that these refinements can influence biologically informed choices of nanodosimetric quantities.<i>Significance</i>. This work provides improved methodology for integration of nanodosimetric parameters into RTP by bridging the gap between nanoscale ionization processes and macroscopic cluster dose calculations.