Purpose:
The purpose of the current study was (1) to develop a straightforward and rapid method to incorporate a dose-averaged linear energy transfer (LETd)–based biological effect model into a dose optimization algorithm for scanned protons; and (2) to apply a novel beam delivery strategy with increased LETd within the target, thereby enhancing the biological effect predicted using the selected relative biological effectiveness (RBE) model.
Materials and Methods:
We first generated pristine dose Bragg curves in water and their corresponding LETd distributions for 94 groups of proton beams, using experimentally validated Geant4 Monte Carlo simulations. Next, we developed 1-dimensional dose optimization algorithms by using the Python programming language. To calculate the RBE of protons for biological dose optimization, we invoked the McNamara RBE model and applied the radiobiological parameters of the lung cancer H460 cell line with 137Cs reference photons.
Results:
High-accuracy optimization results were obtained. The relative difference between the delivered dose and the prescribed dose was approximately within ±1.0% in the target. In addition, we obtained the RBE enhancement within the target by applying the LET-painting technique. For example, considering a simple case in which 2 opposed downslope dose fields were superimposed to form a uniform dose in the 5- to 10-cm target region, the center RBE was 1.23 ± 0.01, which was greater than the center RBE of 1.16 ± 0.01 found when using the traditional method of delivering 2 opposed flat dose fields.
Conclusion:
We have successfully developed an easy-to-implement method to perform the biological dose optimization procedure by invoking the McNamara RBE model in the iteration process using the Python programming language. According to the RBE model predictions, we conclude that the increased target LETd enhances the RBE. The accuracy of the RBE model predictions needs to be validated in radiobiological experiments.