Fada Guan1, Changran Geng2, Duo Ma1, Lawrence Bronk3, Matthew Kerr1, Yuting Li4, Drake Gates5, Benjamin Kroger3, Narayan Sahoo1, Uwe Titt1, David Grosshans3, Radhe Mohan1. 1. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 2. Department of Nuclear Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. 3. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 4. Department of Radiation Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA. 5. Orbital Debris Program Office, NASA Johnson Space Center, Houston, TX, USA.
Abstract
PURPOSE: The purpose of the current study was (1) to develop a straightforward and rapid method to incorporate a dose-averaged linear energy transfer (LET d )-based biological effect model into a dose optimization algorithm for scanned protons; and (2) to apply a novel beam delivery strategy with increased LET d 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 LET d 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 LET d enhances the RBE. The accuracy of the RBE model predictions needs to be validated in radiobiological experiments.
PURPOSE: The purpose of the current study was (1) to develop a straightforward and rapid method to incorporate a dose-averaged linear energy transfer (LET d )-based biological effect model into a dose optimization algorithm for scanned protons; and (2) to apply a novel beam delivery strategy with increased LET d 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 LET d 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 LET d enhances the RBE. The accuracy of the RBE model predictions needs to be validated in radiobiological experiments.
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