Adaptation of stochastic microdosimetric kinetic model for charged-particle therapy treatment planning.

The microdosimetric kinetic (MK) model underestimates the cell-survival fractions for high linear energy transfer (LET) and high dose irradiations. To address the issue, some researchers previously extended the MK model to the stochastic microdosimetric kinetic (SMK) model. In the SMK model, the radiation induced cell-survival fractions were estimated from the specific energies z d and z n absorbed by a microscopic subnuclear structure domain and a cell nucleus, respectively. By taking the stochastic nature of z n as well as that of z d into account, the SMK model could reproduce the measured cell-survival fractions for radiations with wide LET and dose ranges. However, treatment planning based on the SMK model was unrealistic in clinical practice due to its long computation time and huge memory space required for the computation. In this study, we modified the SMK model to shorten the computation time and to reduce the memory space required for the computation. By using the dose-averaged cell-nucleus specific energy per event [Formula: see text] in the SMK formalism, the stochastic nature of z n was reflected onto the estimated cell-survival fractions. The accuracy of the modified SMK model was examined through the comparison between the estimated and the measured survival fractions of human salivary gland tumor cells and V79 cells. We then implemented the modified SMK model into the in-house treatment planning software for scanned charged-particle therapy to validate its applicability in clinical practice. As examples, treatment plans of helium-, carbon-, and neon-ion beams were made for an orbital tumor case. The modified SMK model could reproduce the measured cell-survival fractions more accurately compared to the MK model especially for high-LET and high-dose irradiations. In summary, the modified SMK model offers the accuracy and simplicity required in treatment planning of scanned charged-particle therapy for wide LET and dose ranges.