Standardizing Monte Carlo simulation parameters for a reproducible dose-averaged linear energy transfer. 2020

Wei Yang Calvin Koh, and Hong Qi Tan, and Khong Wei Ang, and Sung Yong Park, and Wen Siang Lew, and James Cheow Lei Lee
Division of Physics and Applied Physics, Nanyang Technological University, Singapore, Singapore.

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. CONCLUSIONS 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. CONCLUSIONS 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.

UI MeSH Term Description Entries
D009010 Monte Carlo Method In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993) Method, Monte Carlo
D011829 Radiation Dosage The amount of radiation energy that is deposited in a unit mass of material, such as tissues of plants or animal. In RADIOTHERAPY, radiation dosage is expressed in gray units (Gy). In RADIOLOGIC HEALTH, the dosage is expressed by the product of absorbed dose (Gy) and quality factor (a function of linear energy transfer), and is called radiation dose equivalent in sievert units (Sv). Sievert Units,Dosage, Radiation,Gray Units,Gy Radiation,Sv Radiation Dose Equivalent,Dosages, Radiation,Radiation Dosages,Units, Gray,Units, Sievert
D011880 Radiotherapy Planning, Computer-Assisted Computer-assisted mathematical calculations of beam angles, intensities of radiation, and duration of irradiation in radiotherapy. Computer-Assisted Radiotherapy Planning,Dosimetry Calculations, Computer-Assisted,Planning, Computer-Assisted Radiotherapy,Calculation, Computer-Assisted Dosimetry,Calculations, Computer-Assisted Dosimetry,Computer Assisted Radiotherapy Planning,Computer-Assisted Dosimetry Calculation,Computer-Assisted Dosimetry Calculations,Dosimetry Calculation, Computer-Assisted,Dosimetry Calculations, Computer Assisted,Planning, Computer Assisted Radiotherapy,Radiotherapy Planning, Computer Assisted
D012062 Relative Biological Effectiveness The ratio of radiation dosages required to produce identical change based on a formula comparing other types of radiation with that of gamma or roentgen rays. Biological Effectiveness, Relative,Effectiveness, Biologic Relative,Effectiveness, Biological Relative,Relative Biologic Effectiveness,Biologic Effectiveness, Relative,Biologic Relative Effectiveness,Biological Relative Effectiveness,Effectiveness, Relative Biologic,Effectiveness, Relative Biological,Relative Effectiveness, Biologic
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D018499 Linear Energy Transfer Rate of energy dissipation along the path of charged particles. In radiobiology and health physics, exposure is measured in kiloelectron volts per micrometer of tissue (keV/micrometer T). Energy Transfer, Linear,LET,Transfer, Linear Energy

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