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Wang Y, Gao J, Tang B, Mo W, Gao H, Guo J, Kong X, Zhang W, Yin Y, Jiao Y, Sun L. A comparative study on the dose-effect of low-dose radiation based on microdosimetric analysis and single-cell sequencing technology. Sci Rep 2024; 14:11524. [PMID: 38773212 PMCID: PMC11109114 DOI: 10.1038/s41598-024-62501-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
The biological mechanisms triggered by low-dose exposure still need to be explored in depth. In this study, the potential mechanisms of low-dose radiation when irradiating the BEAS-2B cell lines with a Cs-137 gamma-ray source were investigated through simulations and experiments. Monolayer cell population models were constructed for simulating and analyzing distributions of nucleus-specific energy within cell populations combined with the Monte Carlo method and microdosimetric analysis. Furthermore, the 10 × Genomics single-cell sequencing technology was employed to capture the heterogeneity of individual cell responses to low-dose radiation in the same irradiated sample. The numerical uncertainties can be found both in the specific energy distribution in microdosimetry and in differential gene expressions in radiation cytogenetics. Subsequently, the distribution of nucleus-specific energy was compared with the distribution of differential gene expressions to guide the selection of differential genes bioinformatics analysis. Dose inhomogeneity is pronounced at low doses, where an increase in dose corresponds to a decrease in the dispersion of cellular-specific energy distribution. Multiple screening of differential genes by microdosimetric features and statistical analysis indicate a number of potential pathways induced by low-dose exposure. It also provides a novel perspective on the selection of sensitive biomarkers that respond to low-dose radiation.
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Affiliation(s)
- Yidi Wang
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Jin Gao
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Bo Tang
- Department of Public Health Surveillance and Evaluation, Shandong Center for Disease Control and Prevention, Jinan, 250014, China
| | - Wei Mo
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Han Gao
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Jiahao Guo
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Xianghui Kong
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Wenyue Zhang
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Yuchen Yin
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China
| | - Yang Jiao
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China.
| | - Liang Sun
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China.
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Kong X, Wang Y, Huang J, Zhang W, Du C, Yin Y, Xue H, Gao H, Liu K, Wu T, Sun L. Microdosimetric assessment about proton spread-out Bragg peak at different depths based on the normal human mesh-type cell population model. Phys Med Biol 2023; 68:175010. [PMID: 37578025 DOI: 10.1088/1361-6560/acec2b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/31/2023] [Indexed: 08/15/2023]
Abstract
Objective.In clinical proton therapy, the spread-out Bragg peak (SOBP) is commonly used to fit the target shape. Dose depositions at microscopic sites vary, even with a consistent absorbed dose (D) in SOBP. In the present study, monolayer mesh-type cell population models were developed for microdosimetric assessment at different SOBP depths.Approach.Normal human bronchial epithelial (BEAS-2B) and hepatocytes (L-O2) mesh-type cell models were constructed based on fluorescence tomography images of normal human cells. Particle transport simulation in cell populations was performed coupled with Monte Carlo software PHITS. The relationship between microdosimetry and macrodosimetry of SOBP at different depths was described by analyzing the microdosimetric indicators such as specific energyz,specific energy distributionfz,D,and relative standard deviationσz/z¯within cells. Additionally, the microdosimetric distributions characteristics and their contributing factors were also discussed.Main results.The microscopic dose distribution is strongly influenced by cellular size, shape, and material. The mean specific energyz¯of nucleus and cytoplasm in the cell population is greater than the overall absorbed dose of the cell population model (Dp), with a maximumz¯/Dpof 1.1. The cellular dose distribution is different between the BEAS-2B mesh-type model and its concentric ellipsoid geometry-type model, which difference inz¯is about 10.3% for the nucleus and about 7.5% for the cytoplasm with the SOBP depth of 15 cm. WhenD= 2 Gy, the maximumzof L-O2 nucleus reaches 2.8 Gy andσz/z¯is 5.1% at the mid-depth SOBP (16-18 cm); while the maximumzof the BEAS-2B nucleus reaches 2.2 Gy with only 2.7% ofσz/z¯.Significance.The significant variation of microdosimetric distributions of SOBP different depths indicates the necessity to use mesh-type cell population models, which have the potential to be compared with biological results and build the bio-physical model.
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Affiliation(s)
- Xianghui Kong
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Yidi Wang
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Jiachen Huang
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Wenyue Zhang
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Chuansheng Du
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Yuchen Yin
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Huiyuan Xue
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Han Gao
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Kun Liu
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Tao Wu
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
| | - Liang Sun
- State Key Laboratory of Radiation Medicine and Protection, Suzhou 215123, People's Republic of China
- School of Radiation Medicine and Protection, Soochow University, Suzhou 215123, People's Republic of China
- Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou 215123, People's Republic of China
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Koh WYC, Tan HQ, Ng YY, Lin YH, Ang KW, Lew WS, Lee JCL, Park SY. Quantifying Systematic RBE-Weighted Dose Uncertainty Arising from Multiple Variable RBE Models in Organ at Risk. Adv Radiat Oncol 2022; 7:100844. [PMID: 35036633 PMCID: PMC8749202 DOI: 10.1016/j.adro.2021.100844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/27/2021] [Accepted: 10/29/2021] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models. METHODS Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming( α β ) x of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties. RESULTS The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique. CONCLUSIONS Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different( α β ) x revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique is recommended for brain tumor when CTV and OARs overlap.
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Affiliation(s)
- Wei Yang Calvin Koh
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Yan Yee Ng
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Yen Hwa Lin
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Khong Wei Ang
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Wen Siang Lew
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore
| | - James Cheow Lei Lee
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Sung Yong Park
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
- Oncology Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore
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Smith EAK, Winterhalter C, Underwood TSA, Aitkenhead AH, Richardson JC, Merchant MJ, Kirkby NF, Kirby KJ, Mackay RI. A Monte Carlo study of different LET definitions and calculation parameters for proton beam therapy. Biomed Phys Eng Express 2021; 8. [PMID: 34874308 DOI: 10.1088/2057-1976/ac3f50] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/02/2021] [Indexed: 12/19/2022]
Abstract
The strongin vitroevidence that proton Relative Biological Effectiveness (RBE) varies with Linear Energy Transfer (LET) has led to an interest in applying LET within treatment planning. However, there is a lack of consensus on LET definition, Monte Carlo (MC) parameters or clinical methodology. This work aims to investigate how common variations of LET definition may affect potential clinical applications. MC simulations (GATE/GEANT4) were used to calculate absorbed dose and different types of LET for a simple Spread Out Bragg Peak (SOBP) and for four clinical PBT plans covering a range of tumour sites. Variations in the following LET calculation methods were considered: (i) averaging (dose-averaged LET (LETd) & track-averaged LET); (ii) scoring (LETdto water, to medium and to mass density); (iii) particle inclusion (LETdto all protons, to primary protons and to particles); (iv) MC settings (hit type and Maximum Step Size (MSS)). LET distributions were compared using: qualitative comparison, LET Volume Histograms (LVHs), single value criteria (maximum and mean values) and optimised LET-weighted dose models. Substantial differences were found between LET values in averaging, scoring and particle type. These differences depended on the methodology, but for one patient a difference of ∼100% was observed between the maximum LETdfor all particles and maximum LETdfor all protons within the brainstem in the high isodose region (4 keVμm-1and 8 keVμm-1respectively). An RBE model using LETdincluding heavier ions was found to predict substantially different LET-weighted dose compared to those using other LET definitions. In conclusion, the selection of LET definition may affect the results of clinical metrics considered in treatment planning and the results of an RBE model. The authors' advocate for the scoring of dose-averaged LET to water for primary and secondary protons using a random hit type and automated MSS.
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Affiliation(s)
- Edward A K Smith
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Carla Winterhalter
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Tracy S A Underwood
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Adam H Aitkenhead
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Jenny C Richardson
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Michael J Merchant
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Norman F Kirkby
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Karen J Kirby
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Ranald I Mackay
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom
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Almhagen E, Traneus E, Ahnesjö A. Handling of beam spectra in training and application of proton RBE models. Phys Med Biol 2021; 66. [PMID: 34464939 DOI: 10.1088/1361-6560/ac226a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/31/2021] [Indexed: 11/11/2022]
Abstract
Published data from cell survival experiments are frequently used as training data for models of proton relative biological effectiveness (RBE). The publications rarely provide full information about the primary particle spectrum of the used beam, or its content of heavy secondary particles. The purpose of this paper is to assess to what extent heavy secondary particles may have been present in published cell survival experiments, and to investigate the impact of non-primary protons for RBE calculations in treatment planning. We used the Monte Carlo code Geant4 to calculate the occurrence of non-primary protons and heavier secondary particles for clinical protons beams in water for four incident energies in the [100, 250] MeV interval. We used the resulting spectra together with a conservative RBE parameterization and an RBE model to map both the rise of RBE at the beam entry surface due to heavy secondary particle buildup, and the difference in estimated RBE if non-primary protons are included or not in the beam quality metric. If included, non-primary protons cause a difference of 2% of the RBE in the plateau region of an spread out Bragg peak and 1% in the Bragg peak. Including non-primary protons specifically for RBE calculations will consequently have a negligible impact and can be ignored. A buildup distance in water of one millimeter was sufficient to reach an equilibrium state of RBE for the four incident energies selected. For the investigated experimental data, 83 out of the 86 data points were found to have been determined with at least that amount of buildup material. Hence, RBE model training data should be interpreted to include the contribution of heavy secondaries.
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Affiliation(s)
- Erik Almhagen
- Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.,The Skandion Clinic, Uppsala, Sweden
| | | | - Anders Ahnesjö
- Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden
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Kalholm F, Grzanka L, Traneus E, Bassler N. A systematic review on the usage of averaged LET in radiation biology for particle therapy. Radiother Oncol 2021; 161:211-221. [PMID: 33894298 DOI: 10.1016/j.radonc.2021.04.007] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 12/20/2022]
Abstract
Linear Energy Transfer (LET) is widely used to express the radiation quality of ion beams, when characterizing the biological effectiveness. However, averaged LET may be defined in multiple ways, and the chosen definition may impact the resulting reported value. We review averaged LET definitions found in the literature, and quantify which impact using these various definitions have for different reference setups. We recorded the averaged LET definitions used in 354 publications quantifying the relative biological effectiveness (RBE) of hadronic beams, and investigated how these various definitions impact the reported averaged LET using a Monte Carlo particle transport code. We find that the kind of averaged LET being applied is, generally, poorly defined. Some definitions of averaged LET may influence the reported averaged LET values up to an order of magnitude. For publications involving protons, most applied dose averaged LET when reporting RBE. The absence of what target medium is used and what secondary particles are included further contributes to an ill-defined averaged LET. We also found evidence of inconsistent usage of averaged LET definitions when deriving LET-based RBE models. To conclude, due to commonly ill-defined averaged LET and to the inherent problems of LET-based RBE models, averaged LET may only be used as a coarse indicator of radiation quality. We propose a more rigorous way of reporting LET values, and suggest that ideally the entire particle fluence spectra should be recorded and provided for future RBE studies, from which any type of averaged LET (or other quantities) may be inferred.
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Affiliation(s)
- Fredrik Kalholm
- Medical Radiation Physics, Dept. of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Leszek Grzanka
- Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland
| | | | - Niels Bassler
- Medical Radiation Physics, Dept. of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden; Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark; Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Koh WYC, Tan HQ, Ang KW, Park SY, Lew WS, Lee JCL. Standardizing Monte Carlo simulation parameters for a reproducible dose-averaged linear energy transfer. Br J Radiol 2020; 93:20200122. [PMID: 32667848 PMCID: PMC7446002 DOI: 10.1259/bjr.20200122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/13/2020] [Accepted: 05/20/2020] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE Dose-averaged linear energy transfer (LETD) is one of the factors which determines relative biological effectiveness (RBE) for treatment planning in proton therapy. It is usually determined from Monte Carlo (MC) simulation. However, no standard simulation protocols were established for sampling of LETD. Simulation parameters like maximum step length and range cut will affect secondary electrons production and have an impact on the accuracy of dose distribution and LETD. We aim to show how different combinations of step length and range cut in GEANT4 will affect the result in sampling of LETD using different MC scoring methods. METHODS In this work, different step length and range cut value in a clinically relevant voxel geometry were used for comparison. Different LETD scoring methods were established and the concept of covariance between energy deposition per step and step length is used to explain the differences between them. RESULTS We recommend a maximum step length of 0.05 mm and a range cut of 0.01 mm in MC simulation as this yields the most consistent LETD value across different scoring methods. Different LETD scoring methods are also compared and variation up to 200% can be observed at the plateau of 80 MeV proton beam. Scoring Method one has one of the lowest percentage differences compared across all simulation parameters. CONCLUSION We have determined a set of maximum step length and range cut parameters to be used for LETD scoring in a 1 mm voxelized geometry. LETD scoring method should also be clearly defined and standardized to facilitate cross-institutional studies. ADVANCES IN KNOWLEDGE Establishing a standard simulation protocol for sampling LETD would reduce the discrepancy when comparing data across different centres, and this can improve the calculation for RBE.
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Affiliation(s)
- Wei Yang Calvin Koh
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
| | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Khong Wei Ang
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Sung Yong Park
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Wen Siang Lew
- Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore
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