Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-23T12:19:30.516Z Has data issue: false hasContentIssue false

Monte Carlo study on mucosal dose in oral and naval cavity using photon beams with small field

Published online by Cambridge University Press:  11 January 2011

James C.L. Chow*
Affiliation:
Department of Radiation Physics, Princess Margaret Hospital, Toronto, Ontario, Canada Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada Department of Physics, Ryerson University, Toronto, Ontario, Canada
Amir M. Owrangi
Affiliation:
Department of Radiation Physics, Princess Margaret Hospital, Toronto, Ontario, Canada
*
Correspondence to: James C.L. Chow, Department of Radiation Physics, Princess Margaret Hospital, Toronto, Ontario, M5G 2M9 Canada. E-mail: [email protected]

Abstract

We study how mucosal dose in the oral or nasal cavity depends on the irradiated small segmental photon fields varying with beam energy, beam angle and mucosa thickness. Dose ratio (mucosal dose with bone underneath to dose at the same point without bone) reflecting the dose enhancement due to the bone backscatter was determined by Monte Carlo simulation (EGSnrc-based code), validated by measurements. Phase space files based on the 6 and 18 MV photon beams with small field size of 1 × 1 cm2, produced by a Varian 21 EX linear accelerator, were generated using the BEAMnrc Monte Carlo code. Mucosa phantoms (mucosa thickness = 1, 2 and 3 mm) with and without a bone under the mucosa were irradiated by photon beams with gantry angles varying from 0 to 30°. Doses along the central beam axis in the mucosa and the dose ratio were calculated with different mucosa thicknesses. For the 6 MV photon beams, the dose at the mucosa-bone interface increased by 44.9–41.7%, when the mucosa thickness increased from 1 to 3 mm for the beam angle ranging from 0 to 30°. These values were lower than those (58.8–53.6%) for the 18 MV photon beams with the same beam angle range. For both the 6 and 18 MV photon beams, depth doses in the mucosa were found to increase with an increase of the beam angle. Moreover, the dose gradient in the mucosa was greater for the 18 MV photon beams compared to the 6 MV. For the dose ratio, it was found that the dose enhancement due to the bone backscatter increased with a decrease of mucosa thickness, and was more significant at both the air-mucosa and mucosa-bone interface. Mucosal dose with bone was investigated by Monte Carlo simulations with different experimental configurations, and was found vary with the beam energy, beam angle and mucosa thickness for a small segmental photon field. The dosimetric information in this study should be considered when searching for an optimized treatment strategy to minimize the mucosal complications in the head-and-neck intensity-modulated radiation therapy.

Type
Original Article
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Jereczek-Fossa, BA, Krengli, M, Orecchia, R. Particle beam radiotherapy for head and neck tumors: radiobiological basis and clinical experience. Head Neck 2006; 28:750760.CrossRefGoogle ScholarPubMed
Lee, N, Puri, DR, Blanco, AI, Chao, KS. Intensity-modulated radiation therapy in head and neck cancers: an update. Head Neck 2007; 29:387400.CrossRefGoogle ScholarPubMed
Ostwald, PM, Kron, T, Hamilton, CS. Assessment of mucosal underdosing in larynx irradiation. Int J Radiat Oncol Biol Phys 1996; 36:181187.CrossRefGoogle ScholarPubMed
Franzmann, EJ, Lundy, DS, Abitbol, AA, Goodwin, WJ. Complete hypopharyngeal obstruction by mucosal adhesions: a complication of intensive chemoradiation for advanced head and neck cancer. Head Neck 2006; 28:663670.CrossRefGoogle ScholarPubMed
Scully, C, Epstein, J, Sonis, S. Oral mucositis: a challenging complication of radiotherapy, chemotherapy, and radiochemotherapy: part 1, pathogenesis and prophylaxis of mucositis. Head Neck 2003; 25:10571070.CrossRefGoogle ScholarPubMed
Scully, C, Epstein, J, Sonis, S. Oral mucositis: a challenging complication of radiotherapy, chemotherapy, and radiochemotherapy. Part 2: diagnosis and management of mucositis. Head Neck 2004; 26:7784.Google ScholarPubMed
Klein, EE, Chin, LM, Rice, RK, Mijnheer, BJ. The influence of air cavities on interface doses for photon beams. Int J Radiat Oncol Biol Phys 1993; 27:419427.CrossRefGoogle ScholarPubMed
Beach, JL, Mendiondo, MS, Mendiondo, OA. A comparison of air-cavity inhomogeneity effects for cobalt-60, 6-, and 10-MV x-ray beams. Med Phys 1987; 14:140144.CrossRefGoogle ScholarPubMed
Kan, WK, Wu, PM, Leung, HT, Lo, TC, Chung, CW, Kwong, DL, Sham, ST. The effect of the nasopharyngeal air cavity on x-ray interface doses. Phys Med Biol 1998; 43:529537.CrossRefGoogle ScholarPubMed
Davidson, SE, Ibbott, GS, Prado, KL, Dong, L, Liao, Z, Followill, DS. Accuracy of two heterogeneity dose calculation algorithms for IMRT in treatment plans designed using an anthropomorphic thorax phantom. Med Phys 2007; 34:18501857.CrossRefGoogle ScholarPubMed
Gagné, IM, Zavgorodni, S. Evaluation of the analytical anisotropic algorithm in an extreme water-lung interface phantom using Monte Carlo dose calculations. J Appl Clin Med Phys 2007; 8:3346.CrossRefGoogle Scholar
Larsson, E, Jönsson, BA, Jönsson, L, Ljungberg, M, Strand, SE. Dosimetry calculations on a tissue level by using the MCNP4c2 Monte Carlo code. Cancer Biother Radiopharm 2005; 20:8591.Google ScholarPubMed
Epp, ER, Boyer, AL, Doppke, KP. Underdosing of lesions resulting from lack of electronic equilibrium in upper respiratory air cavities irradiated by 10MV x-ray beams. Int J Radiat Oncol Biol Phys 1977; 2:613619.CrossRefGoogle ScholarPubMed
Arnfield, MR, Siantar, CH, Siebers, J, Garmon, P, Cox, L, Mohan, R. The impact of electron transport on the accuracy of computed dose. Med Phys 2000; 27:12661274.CrossRefGoogle ScholarPubMed
Starkschall, G, Steadham, RE Jr, Popple, RA, Ahmad, S, Rosen, II. Beam-commissioning methodology for a three-dimensional convolution/superposition photon dose algorithm. J Appl Clin Med Phys 2000; 1:827.Google ScholarPubMed
De Vlamynck, K, Palmans, H, Verhaegen, F, De Wagter, C, De Neve, W, Thierens, H. Dose measurements compared with Monte Carlo simulations of narrow 6 MV multileaf collimator shaped photon beams. Med Phys 1999; 26:18741882.CrossRefGoogle ScholarPubMed
Jones, AO, Das, IJ. Comparison of inhomogeneity correction algorithms in small photon fields. Med Phys 2005; 32:766776.CrossRefGoogle ScholarPubMed
Scott, AJ, Nahum, AE, Fenwick, JD. Monte Carlo modeling of small photon fields: quantifying the impact of focal spot size on source occlusion and output factors, and exploring miniphantom design for small-field measurements. Med Phys 2009; 36:31323144.CrossRefGoogle ScholarPubMed
Jones, AO. A study of the dosimetry of small field photon beams used in intensity modulated radiation therapy in inhomogeneous media: Monte Carlo simulations, and algorithm comparisons and corrections. Med Phys 2004; 31:31613161.CrossRefGoogle Scholar
Chow, JCL, Leung, MK, Van Dyk, J. Variations of lung density and geometry on inhomogeneity correction algorithms: a Monte Carlo dosimetric evaluation. Med Phys 2009; 36:36193630.CrossRefGoogle ScholarPubMed
Fogliata, A, Vanetti, E, Albers, D, Brink, C, Clivio, A, Knöös, T, Nicolini, G, Cozzi, L. On the dosimetric behaviour of photon dose calculation algorithms in the presence of simple geometric heterogeneities: comparison with Monte Carlo calculations. Phys Med Biol 2007; 52:13631385.CrossRefGoogle ScholarPubMed
Krieger, T, Sauer, OA. Monte Carlo- versus pencil-beam-/collapsed-cone-dose calculation in a heterogeneous multi-layer phantom. Phys Med Biol 2005; 50:859868.CrossRefGoogle Scholar
Nelson, WR, Hirayama, H, Rogers, DWO. The EGS4 code system. SLAC Report 265, Stanford Linear Accelerator Center, Stanford, CA, 1965.Google Scholar
Kawrakow, I, Rogers, DWO. The EGSnrc code system: Monte Carlo simulation of electron and photon transport. NRCC Report PIPRS-701, National Research Council of Canada, Ottawa, 2000.Google Scholar
Rogers, DW, Faddegon, BA, Ding, GX, Ma, CM, We, J, Mackie, TR. BEAM: a Monte Carlo code to simulate radiotherapy treatment units. Med Phys 1995; 22:503524.CrossRefGoogle Scholar
Rogers, DWO, Ma, CM, Ding, GX, Walters, B, Sheikh-Bagheri, D, Zhang, GG. BEAMnrc users manual. NRC Report PIRS 509b(revF), 2001.Google Scholar
Ma, CM, Reckwerdt, P, Holmes, M, Rogers, DWO, Gesiser, B. DOSXYZ user manual. NRC Report PIRS 509b, 1995.Google Scholar
Bielajew, AF, Rogers DWO PRESTA-The Parameter Reduced Electron-step Transport Algorithm for electron Monte Carlo transport. Nucl Instrum Methods Phys Res B 1984; 18:535548.Google Scholar
Chow, JCL, Wong, E, Chen, JZ, Van Dyk, J. Comparison of dose calculation algorithms with Monte Carlo methods for photon arcs. Med Phys 2003; 30:26862694.CrossRefGoogle ScholarPubMed
Chow, JCL, Grigorov, GN, Jiang, R. Improved peripheral dose calculation accuracy for a small MLC field brought by the latest commercial treatment planning system. J Radiother Practice 2006; 5:121128.CrossRefGoogle Scholar
Kim, S, Liu, CR, Zhu, TC, Palta, JR. Photon beam skin dose analyses for different clinical setups. Med Phys 1998; 25:860866.CrossRefGoogle ScholarPubMed
Ostwald, PM, Kron, T. Surface dose measurements for highly oblique electron beams. Med Phys 1996; 23:14131420.CrossRefGoogle ScholarPubMed