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Comparison of CCC and ETAR dose calculation algorithms in pituitary adenoma radiation treatment planning; Monte Carlo evaluation

Published online by Cambridge University Press:  28 May 2014


K. Tanha
Affiliation:
Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
S. R. Mahdavi
Affiliation:
Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran
G. Geraily
Affiliation:
Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
Corresponding
E-mail address:

Abstract

Aims

To verify the accuracy of two common absorbed dose calculation algorithms in comparison to Monte Carlo (MC) simulation for the planning of the pituitary adenoma radiation treatment.

Materials and methods

After validation of Linac's head modelling by MC in water phantom, it was verified in Rando phantom as a heterogeneous medium for pituitary gland irradiation. Then, equivalent tissue-air ratio (ETAR) and collapsed cone convolution (CCC) algorithms were compared for a conventional three small non-coplanar field technique. This technique uses 30 degree physical wedge and 18 MV photon beams.

Results

Dose distribution findings showed significant difference between ETAR and CCC of delivered dose in pituitary irradiation. The differences between MC and dose calculation algorithms were 6.40 ± 3.44% for CCC and 10.36 ± 4.37% for ETAR. None of the algorithms could predict actual dose in air cavity areas in comparison to the MC method.

Conclusions

Difference between calculation and true dose value affects radiation treatment outcome and normal tissue complication probability. It is of prime concern to select appropriate treatment planning system according to our clinical situation. It is further emphasised that MC can be the method of choice for clinical dose calculation algorithms verification.


Type
Original Articles
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Wieslander, E, Knöös, T. Monte Carlo based verification of a beam model used in a treatment planning system. Journal of Physics: Conference Series, IOP Publishing, 2008; 102 (1).Google Scholar
2. Arnfield, M R, Siantar, C H, Siebers, J, Garmon, P, Cox, L, Mohan, R. The impact of electron transport on the accuracy of computed dose. Med Phys 2000; 27 (6): 12661274.CrossRefGoogle ScholarPubMed
3. Engelsman, M, Damen, E M, Koken, P W, van‘t Veld, A A, van Ingen, K M, Mijnheer, B J. Impact of simple tissue inhomogeneity correction algorithms on conformal radiotherapy of lung tumours. Radiother Oncol 2001; 60 (3): 299309.CrossRefGoogle ScholarPubMed
4. Hurkmans, C, Knöös, T, Nilsson, P, Svahn-Tapper, G, Danielsson, H. Limitations of a pencil beam approach to photon dose calculations in the head and neck region. Radiother Oncol 1995; 37 (1): 7480.CrossRefGoogle ScholarPubMed
5. Jones, B, Aird, E, Colyer, H. et al. United Kingdom Radiation Oncology 1 Conference (UKRO 1): accuracy and uncertainty in radiotherapy. 2014.Google Scholar
6. Sharpe, M, Battista, J. Dose calculations using convolution and superposition principles: The orientation of dose spread kernels in divergent x-ray beams. Med Phys 1993; 20 (6): 16851694.CrossRefGoogle ScholarPubMed
7. Vanderstraeten, B, Reynaert, N, Paelinck, L et al. Accuracy of patient dose calculation for lung IMRT: A comparison of Monte Carlo, convolution/superposition, and pencil beam computations. Med Phys 2006; 33 (9): 31493158.CrossRefGoogle ScholarPubMed
8. Francescon, P, Cora, S, Chiovati, P. Dose verification of an IMRT treatment planning system with the BEAM EGS4-based Monte Carlo code. Med Phys 2003; 30 (2): 144157.CrossRefGoogle ScholarPubMed
9. Shortt, K, Ross, C, Bielajew, A, Rogers, D. Electron beam dose distributions near standard inhomogeneities. Phys Med Biol 1986; 31 (3): 235249.CrossRefGoogle Scholar
10. Li, J, Pawlicki, T, Deng, J, Jiang, S, Mok, E, Ma, C. Validation of a Monte Carlo dose calculation tool for radiotherapy treatment planning. Phys Med Biol 2000; 45 (10): 29692985.CrossRefGoogle ScholarPubMed
11. Bielajew, A, Rogers, D. A standard timing benchmark for EGS4 Monte Carlo calculations. Med Phys 1992; 19 (2): 303304.CrossRefGoogle ScholarPubMed
12. Verhaegen, F, Seuntjens, J. Monte Carlo modelling of external radiotherapy photon beams. Phys Med Biol 2003; 48 (21): R107R163.CrossRefGoogle ScholarPubMed
13. Mohan, R, Antolak, J, Hendee, W R. Monte Carlo techniques should replace analytical methods for estimating dose distributions in radiotherapy treatment planning. Med Phys 2001; 28: 123126.CrossRefGoogle ScholarPubMed
14. Rogers, D. The role of Monte Carlo simulation of electron transport in radiation dosimetry. Int J Rad Appl Instrum A 1991; 42 (10): 965974.CrossRefGoogle ScholarPubMed
15. Kawrakow, I. Accurate condensed history Monte Carlo simulation of electron transport. I. egs nrc, the new egs4 version. Med Phys 2000; 27: 485498.CrossRefGoogle Scholar
16. Rogers, D W O, Walters, B, Kawrakow, I. BEAMnrc users manual. NRC report PIRS-0509(A)revK. Ottawa (ON): National Research Council of Canada, 2005.Google Scholar
17. Bielajew A, Wiebe P. EGS_Windows—a graphical interface to EGS, NRCC Report: PIRS-0274 1991.Google Scholar
18. Ma, C M, Reckwerdt, P J, Holmes, M, Rogers, D W O, Geiser, B. DOSXYZ Users Manual, National Research Council of Canada Report No. PIRS-509B. Ottawa, Canada: NRCC, 1995.Google Scholar
19. Sheikh-Bagheri, D, Rogers, D. Sensitivity of megavoltage photon beam Monte Carlo simulations to electron beam and other parameters. Med Phys 2002; 29: 379390.CrossRefGoogle ScholarPubMed
20. Pemler, P, Besserer, J, Schneider, U, Neuenschwander, H. Evaluation of a commercial electron treatment planning system based on Monte Carlo techniques (eMC). Zeitschrift fur medizinische Physik 2005; 16 (4): 313329.CrossRefGoogle Scholar
21. Cheung, T, Butson, M J, Peter, K. Post-irradiation colouration of Gafchromic EBT radiochromic film. Phys Med Biol 2005; 50 (20): N281N285.CrossRefGoogle ScholarPubMed
22. Kihlen, B, Rudén, B I. Reproducibility of Field Alignment in Radiation therapy: A Large-Scale Clinical Experience. Acta Oncologica 1989; 28 (5): 689692.CrossRefGoogle ScholarPubMed
23. Ludlow, J B, Ivanovic, M. Comparative dosimetry of dental CBCT devices and 64-slice CT for oral and maxillofacial radiology. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008; 106 (1): 106114.CrossRefGoogle ScholarPubMed
24. Ding, G X. Energy spectra, angular spread, fluence profiles and dose distributions of 6 and 18 MV photon beams: results of Monte Carlo simulations for a Varian 2100EX accelerator. Phys Med Biol 2002; 47 (7): 10251046.CrossRefGoogle ScholarPubMed
25. Siantar, C L H, Walling, R, Daly, T et al. Description and dosimetric verification of the peregrine Monte Carlo dose calculation system for photon beams incident on a water phantom. Med Phys 2001; 28: 13221337.CrossRefGoogle Scholar
26. Jaffray, D A, Battista, J J, Fenster, A, Munro, P. X-ray sources of medical linear accelerators: focal and extra-focal radiation. Med Phys 1993; 20 (5): 14171427.CrossRefGoogle ScholarPubMed
27. Venselaar, J, Welleweerd, H, Mijnheer, B. Tolerances for the accuracy of photon beam dose calculations of treatment planning systems. Radiother Oncol 2001; 60 (2): 191201.CrossRefGoogle ScholarPubMed
28. ICRU, I. Report 50. Prescribing, recording and reporting photon beam therapy (International Commission on Radiation Units and Measurements, Bethesda, MD, 1993), 1993.Google Scholar
29. Units, I.C.f.R. and Measurements, ICRU Report 62. Prescribing, recording, and reporting photon beam therapy (Supplement to ICRU Report 50). ICRU Bethesda, MD, 1999.Google Scholar
30. Hendee, W R, Ibbott, G S, Hendee, E G. Radiation therapy physics, John Wiley & Sons, 2013.Google Scholar
31. Drzymala, R, Mohan, R, Brewster, L et al. Dose-volume histograms. Int J Radiat Oncol Biol Phys 1991; 21 (1): 7178.CrossRefGoogle ScholarPubMed
32. Mohan, R, Brewster, L J, Barest, G D. A technique for computing dose volume histograms for structure combinations. Med Phys 1987; 14: 10481052.CrossRefGoogle ScholarPubMed
33. Andreo, P. Monte Carlo techniques in medical radiation physics. Phys Med Biol 2000; 36 (7): 861920.CrossRefGoogle ScholarPubMed
34. Brualla, L, Palanco-Zamora, R, Steuhl, K P, Bornfeld, N, Sauerwein, W. Monte Carlo simulations applied to conjunctival lymphoma radiotherapy treatment. Strahlentherapie und Onkologie 2011; 187 (8): 492498.CrossRefGoogle ScholarPubMed
35. Dobler, B, Walter, C, Knopf, A et al. Optimization of extracranial stereotactic radiation therapy of small lung lesions using accurate dose calculation algorithms. Radiat Oncol 2006; 1 (4).CrossRefGoogle ScholarPubMed
36. Todorovic, M, Fischer, M, Cremers, F, Thom, E, Schmidt, R. Evaluation of GafChromic EBT prototype B for external beam dose verification. Med Phys 2006; 33: 13211328.CrossRefGoogle ScholarPubMed
37. Devic, S, Seuntjens, J, Sham, E et al. Precise radiochromic film dosimetry using a flat-bed document scanner. Med Phys 2005; 32: 22452253.CrossRefGoogle ScholarPubMed
38. Devic, S, Seuntjens, J, Hegyi, G et al. Dosimetric properties of improved GafChromic films for seven different digitizers. Med Phys 2004; 31: 23922401.CrossRefGoogle ScholarPubMed
39. Chow, J C L, Jiang, R, Leung, M K K. Dosimetry of oblique tangential photon beams calculated by superposition/convolution algorithms: a Monte Carlo evaluation. J Appl Clin Med Phys 2011; 12: 108121.CrossRefGoogle ScholarPubMed
40. Polednik, M, Abo Madyan, Y, Schneider, F et al. Evaluation of calculation algorithms implemented in different commercial planning systems on an anthropomorphic breast phantom using film dosimetry. Strahlentherapie und Onkologie 2007; 183 (12): 667672.CrossRefGoogle ScholarPubMed
41. Calvo, O I, Gutiérrez, A N, Stathakis, S, Esquivel, C, Papanikolaou, N. On the quantification of the dosimetric accuracy of collapsed cone convolution superposition (CCCS) algorithm for small lung volumes using IMRT. J Appl Clin Med Phys 2012; 13 (3): 3751.CrossRefGoogle ScholarPubMed
42. Podgorsak, E B. Radiation Oncology Physics: A Handbook for Teachers and Students. Vienna, Austria: IAEA, 2005.Google Scholar
43. da Rosa, L A, Cardoso, S C, Campos, L T, Alves, V G, Batista, D V, Facure, A. Percentage depth dose in heterogeneous media using thermoluminescent dosimetry. J Appl Clin Med Phys 2010; 11: 117127.CrossRefGoogle ScholarPubMed
44. Du Plessis, F, Willemse, C, Lötter, M, Goedhals, L. Comparison of the Batho, ETAR and Monte Carlo dose calculation methods in CT based patient models. Med Phys 2001; 28: 582589.CrossRefGoogle ScholarPubMed
45. Cedric, X Y, Wong, J W. Implementation of the ETAR method for 3D inhomogeneity correction using FFT. Med Phys 1993; 20: 627632.Google Scholar
46. Shih, R, Li, X A, Chu, J C H. Dynamic wedge versus physical wedge: a Monte Carlo study. in Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE, IEEE, 2000.Google Scholar
47. Ahmad, M, Hussain, A, Muhammad, W, Rizvi, S Q, Matiullah, . Studying wedge factors and beam profiles for physical and enhanced dynamic wedges. J Med Phys 2010; 35 (1): 3341.CrossRefGoogle ScholarPubMed
48. Ahnesjö, A. Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media. Med Phys 1989; 16: 577592.CrossRefGoogle ScholarPubMed
49. Wieslander, E, Knöös, T. Dose perturbation in the presence of metallic implants: treatment planning system versus Monte Carlo simulations. Phys Med Biol 2003; 48 (20): 32953305.CrossRefGoogle ScholarPubMed
50. Carrasco, P, Jornet, N, Duch, M et al. Comparison of dose calculation algorithms in phantoms with lung equivalent heterogeneities under conditions of lateral electronic disequilibrium. Med Phys 2004; 31: 28992911.CrossRefGoogle ScholarPubMed
51. Jones, A O, Das, I J. Comparison of inhomogeneity correction algorithms in small photon fields. Med Phys 2005; 32: 766776.CrossRefGoogle ScholarPubMed
52. Hasenbalg, F, Neuenschwander, H, Mini, R, Born, E. Collapsed cone convolution and analytical anisotropic algorithm dose calculations compared to VMC++ Monte Carlo simulations in clinical cases. Phys Med Biol 2007; 52 (13): 36793691.CrossRefGoogle ScholarPubMed
53. Fotina, I, Winkler, P, Künzler, T, Reiterer, J, Simmat, I, Georg, D. Advanced kernel methods vs. Monte Carlo-based dose calculation for high energy photon beams. Radiother Oncol 2009; 93 (3): 645653.Google ScholarPubMed
54. Roberts, R. How accurate is a CT-based dose calculation on a pencil beam TPS for a patient with a metallic prosthesis? Phys Med Biol 2001; 46 (9): N227N234.CrossRefGoogle ScholarPubMed

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