Volume 25, Issue 11 (2-2019)
RJMS 2019, 25(11): 85-97 |
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Rezaee L. Comparison of the physical and biological dose of oxygen ions and proton in hadron therapy
. RJMS 2019; 25 (11) :85-97
URL: http://rjms.iums.ac.ir/article-1-5276-en.html
URL: http://rjms.iums.ac.ir/article-1-5276-en.html
Shiraz Branch, Islamic Azad University, Shiraz, Iran , rezaie_l@iaushiraz.ac.ir
Abstract: (5097 Views)
Background: In radiation therapy, oxygen ions have more biological benefits than lighter ions such as proton. Oxygen has a higher Linear Energy Transfer (LET) and a larger Relative Biological Effectiveness (RBE). To design the Spread-Out Bragg Peak (SOBP) of biological doses, we have developed a functional approach with Monte Carlo calculations and matrix computations. We have used this method for both oxygen and proton beams.
Methods: After obtaining the profiles of the Bragg Peak by Geant4 code, intensity weighing factors for each beam was calculated to create a uniform SOBP. Also, the RBE value was calculated according to the Linear-uQadratic model (LQ). Biological dose, physical dose, and cell survival levels were also obtained in the radiation of both ions.
Results: The designed biological SOBP has a good uniformity. Physical dose derived from proton and oxygen beams do not differ significantly, but for biological dose, there is a sharp difference between them. Even with the modulation of the intensity of the beams to produce the same biological dosage, the cell survival levels chart will vary greatly.
Conclusion: The biological properties and effects of oxygen in respect to the proton can be a good choice to optimize the system to maximize damage to the tumor tissue and minimize damage to surrounding healthy tissues. The existence of richer tables of experimental values for the effective parameters on the amount of relative biological effectiveness greatly increases the accuracy of this optimization.
Methods: After obtaining the profiles of the Bragg Peak by Geant4 code, intensity weighing factors for each beam was calculated to create a uniform SOBP. Also, the RBE value was calculated according to the Linear-uQadratic model (LQ). Biological dose, physical dose, and cell survival levels were also obtained in the radiation of both ions.
Results: The designed biological SOBP has a good uniformity. Physical dose derived from proton and oxygen beams do not differ significantly, but for biological dose, there is a sharp difference between them. Even with the modulation of the intensity of the beams to produce the same biological dosage, the cell survival levels chart will vary greatly.
Conclusion: The biological properties and effects of oxygen in respect to the proton can be a good choice to optimize the system to maximize damage to the tumor tissue and minimize damage to surrounding healthy tissues. The existence of richer tables of experimental values for the effective parameters on the amount of relative biological effectiveness greatly increases the accuracy of this optimization.
Keywords: Hadron therapy, Geant4 Code, Physical dose, Biological dose, Relative biological effectiveness
Type of Study: Research |
Subject:
Biophysics
References
1. 1. Loeffler JS, Durante M. Charged particle therapy—optimization, challenges and future directions. Nat Rev Clin Oncol; 2013. 10:411-24.
2. 2. Kempe I, Gudowska I, Brahme A. Depth absorbed dose and LET distributions of therapeutic 1H, 4He, 7Li, and 12C beams. Med Phys; 2007. 34:183-92.
3. 3. Kantemiris I, Karaiskos P, Papagiannis P, Angelopoulos A. Dose and dose averaged LET comparison of (1)H, (4)He, (6)Li (8)Be, (1)(0)B, (1)(2)C, (1)(4)N, and (1)(6)O ion beams forming a spread-out Bragg peak. Med Phys; 2011. 38: 6585-1.
4. 4. Pshenichnov I, Mishustin I, Greiner W. Comparative study of depth–dose distributions for beams of light and heavy nuclei in tissue-like media. Nucl Instrum Methods Phys Res B; 2008. 266:1094-8.
5. 5. Romano F, Cirrone GA, Cuttone G, Rosa FD, Mazzaglia SE, Petrovic I, et al. A Monte Carlo study for the calculation of the average linear energy transfer (LET) distributions for a clinical proton beam line and a radiobiological carbon ion beam line. Phy Med Bio; 2014. 59: 2863-82.
6. 6. Tessonnier T, Mairani A, Chen W, Sala P, Cerutti F, Ferrari A, et al. Proton and helium ion radiotherapy for meningioma tumors: a Monte Carlo-based treatment planning comparison. Rad Oncol; 2018. 3:12-7.
7. 7. Fuchs H, Strobele J, Schreiner T, Hirtl A, Georg D. A pencil beam algorithm for helium ion beam therapy. Med Phys; 2012. 39:6726-37.
8. 8. Kurz C, Mairani A, Parodi K. First experimental-based characterization of oxygen ion beam depth dose distributions at the Heidelberg Ion-Beam Therapy Center. Phys Med Biol; 2012. 57:5017-34.
9. 9. Tommasino F, Scifoni E, Durante M. New ions for therapy. Int J Particle Ther; 2015. 428-38.
10. 10. Mortazavi SMJ, Hosseini SMA, Jia SB, Mahdavi SR, Mehdizadeh AR. A scientific review on hadron therapy. Razi J Med Sci; 2017. 23:52-67.
11. 11. Bortfeld T, Schlegel W. An analytical approximation of depth–dose distributions for therapeutic proton beams. Phy Med Bio; 1996. 41:1331-9.
12. 12. Rezaee L. Design of spread-out Bragg peaks in hadron therapy with oxygen ions. Rep Prac Oncol Rad; 2018. 23:433-41.
13. 13. Schultheiss TE, Zagars GK, Peters LJ. An explanatory hypothesis for early-and late-effect parameter values in the LQ model. Radiother Oncol; 1987. 9:241.
14. 14. Kramer M, Scholz M. The increased biological effectiveness of heavy charged particles: from radiobiology to treatment planning. Phy Med Bio; 2000. 45:3319.
15. 15. Habermehl D, Ilicic K, Dehne S, Rieken S, Orschiedt L, Brons S, et al. The relative biological effectiveness for carbon and oxygen ion beams using the raster-scanning technique in hepatocellular carcinoma cell lines. Plos One; 2014. 9:113591.
16. 16. Paganetti H. Relative biological effectiveness (RBE) values for proton beam therapy. Variations as a function of biological endpoint, dose, and linear
17. energy transfer. Phy Med Bio; 2014. 59:419.
18. 17. Agostinelli S, Allison J, Amako K. Geant4—a simulation toolkit. Nucl Instr Meth Phys Res A; 2003. 506:250-303.
19. 18. Zirkle RE, Marchbank DF, Kuck KD. Exponential and sigmoid survival curves resulting from alpha and X irradiation of aspergillus spores. J Cellular Com Physiol; 1952. 39:75.
20. 19. Grzanka L. Modelling beam transport and biological effectiveness develop treatment planning for ion beam radiotherapy. PhD Thesis. 2013.
21. 20. Scholz M, Kellerer AM, Kraft-Weyrather W, Kraft G. Computation of cell survival in heavy ion beams for therapy. The model and its approximation, Radiat Environ Biophys; 1997. 36:59.
22. 21. Kelley MR. DNA repair in cancer therapy: molecular targets and clinical applications. Amsterdam: Academic Press; 2012.
23. 22. Tessonnier T. Treatment of low-grade meningiomas with protons and helium ions. PhD Thesis. 2017.
24. 23. Yuki K, Kanematsu N, Kanai T, Matsufuji N. Biological dose calculation with Monte Carlo physics simulation for heavy-ion radiotherapy. Phy Med Bio; 2006. 51:467-75.
25. 24. Hosseini MA, Jia SB, Ebrahimi-Loushab M. Analysis of relative biological effectiveness of proton beams and isoeffective dose profiles using Geant4. J Biomed Phys Eng; 2017. 7:95-100.
26. 25. Jette D, Chen W. Creating a spread-out Bragg peak in proton beams. Phy Med Bio; 2011. 56:131-8.
27. 26. Słonina D, Biesaga B, Swakoń J, Kabat D, Grzanka L, Ptaszkiewicz M, et al. Relative biological effectiveness of the 60-MeV therapeutic proton beam at the Institute of Nuclear Physics (IFJ PAN) in Kraków, Poland. Radiat Environ Biophys; 2014. 53:745.
28. 27. Bert C, Engenhart-Cabillic R, Durante M. Particle therapy for noncancer diseases. Med Phy; 2012. 39:1716-27.
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