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Prediction of Dose-Averaged linear energy transfer in lung for carbon-ion radiotherapy
Zhiyuan Yang1, Weihai Zhuo1, Zhou Yuan1
1Institute of Radiation Medicine, Fudan University, 2094 Xietu Road, Shanghai 200032, China.
Background:
Carbon ions exhibit superior biological effects compared to protons in radiation therapy owing to their high linear energy transfer (LET) characteristics. The dose-averaged LET (LETd) serves as a critical parameter for evaluating the efficacy of carbon ion radiotherapy (CIRT).
Purpose:
The study aims to investigate the modulation of LETd distributions of carbon ions in lung tissue by developing a convolution-based model to estimate both dose and LETd distributions within porous materials.
Methods:
An extended convolutional model was developed to calculate the dose and LETd distributions of carbon ion beams in porous material. The new model was preliminarily validated through Monte Carlo (MC) simulations and experimental measurements conducted in lung equivalent material and fresh porcine lung tissue.
Results:
In MC simulations, the maximum relative error between the model-predicted dose and the simulated values in the Bragg peak region was 0.97%.The average LETd prediction error was 3.96%. In LN300, the model predictions exhibited an average LETd error of 7.03% relative to MC simulations and 14.45% relative to measured values. In fresh porcine lungs, the model predictions showed good agreement with experimental measurements.
Conclusion:
The enhanced convolutional model demonstrated accurate prediction of both dose and LETd distributions of carbon beams in porous materials. These results suggest that the model has strong potential as an effective tool for optimising clinical carbon ion radiotherapy treatment planning.
