RBE model-based biological dose optimization for proton radiobiology studies

Fada Guan; Changran Geng; Duo Ma; Lawrence Bronk; Matthew Kerr; Yuting Li; Drake Gates; Benjamin Kroger; Narayan Sahoo; Uwe Titt; David Grosshans; Radhe Mohan

doi:10.14338/IJPT-18-00007.1

RBE model-based biological dose optimization for proton radiobiology studies

Fada Guan, Changran Geng, Duo Ma, Lawrence Bronk, Matthew Kerr, Yuting Li, Drake Gates, Benjamin Kroger, Narayan Sahoo, Uwe Titt, David Grosshans, Radhe Mohan

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

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 (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 61.0% in the target. In addition, we obtained the RBE enhancement within the target by applying the LETpainting 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 6 0.01, which was greater than the center RBE of 1.16 6 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.

Original language	English (US)
Pages (from-to)	160-171
Number of pages	12
Journal	International Journal of Particle Therapy
Volume	5
Issue number	1
DOIs	https://doi.org/10.14338/IJPT-18-00007.1
State	Published - Jun 1 2019

Keywords

Monte Carlo
Python
biological effect
proton therapy
ramped dose fields

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging

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10.14338/IJPT-18-00007.1

Cite this

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title = "RBE model-based biological dose optimization for proton radiobiology studies",

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 (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 61.0% in the target. In addition, we obtained the RBE enhancement within the target by applying the LETpainting 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 6 0.01, which was greater than the center RBE of 1.16 6 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.",

keywords = "Monte Carlo, Python, biological effect, proton therapy, ramped dose fields",

author = "Fada Guan and Changran Geng and Duo Ma and Lawrence Bronk and Matthew Kerr and Yuting Li and Drake Gates and Benjamin Kroger and Narayan Sahoo and Uwe Titt and David Grosshans and Radhe Mohan",

note = "Funding Information: Conflicts of Interest: Drs. Radhe Mohan and David Grosshans are editors for the International Journal of Particle Therapy. All other authors have no conflicts of interest to disclose. Acknowledgments: This work was supported by the National Cancer Institute (Grant No. U19 CA021239) and by an Institutional Research Seed Grant from The University of Texas MD Anderson Cancer Center. We thank Dr. Aimee McNamara for sharing her knowledge in RBE modelling and Erica Goodoff for her editorial assistance. Publisher Copyright: {\textcopyright} 2018 International Journal of Particle Therapy.",

year = "2019",

month = jun,

day = "1",

doi = "10.14338/IJPT-18-00007.1",

language = "English (US)",

volume = "5",

pages = "160--171",

journal = "International Journal of Particle Therapy",

issn = "2331-5180",

publisher = "Allen Press Inc.",

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T1 - RBE model-based biological dose optimization for proton radiobiology studies

AU - Guan, Fada

AU - Geng, Changran

AU - Ma, Duo

AU - Bronk, Lawrence

AU - Kerr, Matthew

AU - Li, Yuting

AU - Gates, Drake

AU - Kroger, Benjamin

AU - Sahoo, Narayan

AU - Titt, Uwe

AU - Grosshans, David

AU - Mohan, Radhe

N1 - Funding Information: Conflicts of Interest: Drs. Radhe Mohan and David Grosshans are editors for the International Journal of Particle Therapy. All other authors have no conflicts of interest to disclose. Acknowledgments: This work was supported by the National Cancer Institute (Grant No. U19 CA021239) and by an Institutional Research Seed Grant from The University of Texas MD Anderson Cancer Center. We thank Dr. Aimee McNamara for sharing her knowledge in RBE modelling and Erica Goodoff for her editorial assistance. Publisher Copyright: © 2018 International Journal of Particle Therapy.

PY - 2019/6/1

Y1 - 2019/6/1

N2 - 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 61.0% in the target. In addition, we obtained the RBE enhancement within the target by applying the LETpainting 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 6 0.01, which was greater than the center RBE of 1.16 6 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.

AB - 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 61.0% in the target. In addition, we obtained the RBE enhancement within the target by applying the LETpainting 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 6 0.01, which was greater than the center RBE of 1.16 6 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.

KW - Monte Carlo

KW - Python

KW - biological effect

KW - proton therapy

KW - ramped dose fields

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JO - International Journal of Particle Therapy

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RBE model-based biological dose optimization for proton radiobiology studies

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