Calculation of Involved and Noninvolved Organs Doses in Carbon Therapy of Brain Tumor Using GEANT4 Simulation Toolkit

被引:0
作者
Ahmadi, Maria [1 ]
Motevalli, S. Mohammad [1 ]
Taherparvar, Payvand [2 ]
Zanganeh, Vahid [3 ]
机构
[1] Department of Physics, Faculty of Science, University of Mazandaran, Babolsar
[2] Department of Physics, Faculty of Science, University of Guilan, Rasht
[3] Department of Physics, Faculty of Science, Golestan University, Gorgan
关键词
Brain; Heavy Ion Radiotherapy; Monte Carlo Simulation; SOBP;
D O I
10.22038/ijmp.2023.71006.2251
中图分类号
学科分类号
摘要
Introduction: This study used the GEANT4 Monte Carlo toolkit for radiation transport simulations in brain carbon therapy, incorporating a human phantom model to accurately assess dose delivery to targeted and non-targeted organs. Weight factors were employed to generate a Spread Out Bragg Peak (SOBP). Material and Methods: The study used the ORNL-MIRD phantom to simulate carbon therapy for brain tumors, finding that the optimal energy range for carbon ions was 2420–2560 MeV to effectively cover the tumor. To achieve a homogeneous radiation dose, a Spread Out ragg Peak (SOBP) was generated using multiple Bragg peaks with specific intensity factors. Beam parameters were also evaluated per ICRU guidelines. Results: This study estimated the flux and dose distributions of secondary particles—protons, electrons, neutrons, alpha particles, and photons—in the brain tumor and surrounding tissues. We calculated the cumulative dose from both carbon ions and secondary particles, finding an absorbed dose ratio of 0.003 in healthy brain tissue compared to the tumor, with values of 4.8 × 10-4 for the skull and 2.6 × 10-5 for the thyroid. Notably, neutrons and photons can significantly increase energy transfer to distant organs, raising secondary cancer risk. Conclusion: The findings presented in this article demonstrated that the involvement of secondary particles in the dose received by both the brain and other organs remains minimal, as the highest absorbed dose was predominantly localized within the tumor. © (2024), (Mashhad University of Medical Sciences). All rights reserved.
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页码:287 / 294
页数:7
相关论文
共 38 条
[1]  
Mitin T, Zietman AL., Promise and pitfalls of heavy-particle therapy, J Clin Oncol, 32, 26, (2014)
[2]  
Tommasino F, Scifoni E, Durante M., New ions for therapy, Int J Particle Ther, 2, 3, pp. 428-438, (2015)
[3]  
Mihai M, Spunei M, Malaescu I., Comparison features for proton and heavy ion beams versus photon and electron beams, Rom Rep Phys, 66, 1, pp. 212-222, (2014)
[4]  
Jiang F, Song YT, Zheng JX, Zeng XH, Wang PY, Zhang JS, Zhang WQ., Energy loss of degrader in SC200 proton therapy facility, Nucl Sci Tech, 30, 1, pp. 1-8, (2019)
[5]  
Hong L, Goitein M, Bucciolini M, Comiskey R, Gottschalk B, Rosenthal S, Et al., A pencil beam algorithm for proton dose calculations, Phys Med Biol, 41, 8, (1996)
[6]  
Jia Y, Beltran C, Indelicato DJ, Flampouri S, Li Z, Merchant TE., Proton therapy dose distribution comparison between Monte Carlo and a treatment planning system for pediatric patients with ependymoma, Med Phys, 39, 8, pp. 4742-4747, (2012)
[7]  
Larsson B., Proton and heavy ion therapy, Seventh International Conference on Cyclotrons and their Applications, pp. 414-418, (1975)
[8]  
Enferadi M, Sarbazvatan S, Sadeghi M, Hong JH, Tung CJ, Chao TC, Wey SP., Nuclear reaction cross sections for proton therapy applications, J Radioanal Nucl Chem, 314, 2, pp. 1207-1235, (2017)
[9]  
Mahdipour SA, Mowlavi AA., Ion therapy for uveal melanoma in new human eye phantom based on GEANT4 toolkit, Med Dosim, 41, 2, pp. 118-125, (2016)
[10]  
Bagheri R, Moghaddam AK, Azadbakht B, Akbari MR, Shirmardi SP., Determination of water equivalent ratio for some dosimetric materials in proton therapy using MNCPX simulation tool, Nucl Sci Tech, 30, 2, (2019)