Volume 32, Issue 7 (October 2021)
Studies in Medical Sciences 2021, 32(7): 558-571 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Zeinali A, Kargar N. COMPARISON OF THE PERFORMANCE OF MONTE-CARLO AND COLLAPSED CONE ALGORITHMS USED IN MONACO TREATMENT PLANNING SYSTEM IN PREDICTING CARDIOPULMONARY COMPLICATIONS DUE TO THE LEFT BREAST RADIOTHERAPY. Studies in Medical Sciences 2021; 32 (7) :558-571
URL: http://umj.umsu.ac.ir/article-1-5434-en.html
URL: http://umj.umsu.ac.ir/article-1-5434-en.html
Department of Medical Physics, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran (Corresponding Author) , niloofar.jeddi@yahoo.com
Abstract: (1905 Views)
Background & Aims: The aim of this study was to evaluate the performance of dose calculation algorithms used in the Monaco treatment planning system to predict cardiopulmonary complications due to left breast radiotherapy.
Materials & Methods: Three-dimensional dose distribution of 21 patients with left breast cancer was prepared by two-dose calculation algorithms (CC and MC) with the same unit monitor. Cardiopulmonary complications due to radiation therapy in these patients were evaluated by different radiobiological models as well as various parameters extracted from previous studies using MATLAB software. In this study, MC dose calculation is considered as benchmark data. Algorithms, Friedman nonparametric test, and Wilcoxon test were used for statistical comparison of the obtained results.
Results: For both dose calculation algorithms, the value of TCP was estimated to be acceptable, with the same parameter being higher for the Poisson model than for the Niemierko model. The difference in NTCP for CC and MC algorithms for pulmonary pneumonitis, pericarditis, and cardiac mortality is not statistically significant by most parameters.
Conclusion: Both dose calculation algorithms estimate the TCP value as acceptable and the NTCP calculated by these two algorithms is close to the expected NTCP value. The value obtained for TCP, and NTCP depends on the radiobiological parameters used in the mathematical formula and the amount of dose extracted from the dose calculation algorithms.
Materials & Methods: Three-dimensional dose distribution of 21 patients with left breast cancer was prepared by two-dose calculation algorithms (CC and MC) with the same unit monitor. Cardiopulmonary complications due to radiation therapy in these patients were evaluated by different radiobiological models as well as various parameters extracted from previous studies using MATLAB software. In this study, MC dose calculation is considered as benchmark data. Algorithms, Friedman nonparametric test, and Wilcoxon test were used for statistical comparison of the obtained results.
Results: For both dose calculation algorithms, the value of TCP was estimated to be acceptable, with the same parameter being higher for the Poisson model than for the Niemierko model. The difference in NTCP for CC and MC algorithms for pulmonary pneumonitis, pericarditis, and cardiac mortality is not statistically significant by most parameters.
Conclusion: Both dose calculation algorithms estimate the TCP value as acceptable and the NTCP calculated by these two algorithms is close to the expected NTCP value. The value obtained for TCP, and NTCP depends on the radiobiological parameters used in the mathematical formula and the amount of dose extracted from the dose calculation algorithms.
Keywords: Treatment Planning System, Breast Cancer, Dose Calculation Algorithm, Normal Tissue Complication Probability (NTCP), Tumor Control Probability (TCP)
Type of Study: Research |
Subject:
فیزیک پزشکی
References
1. Wang J, Li X, Deng Q, Xia B, Wu S, Liu J, et al. Postoperative radiotherapy following mastectomy for patients with left-sided breast cancer: A comparative dosimetric study. Med Dosim 2015;40(3):190-4. [DOI:10.1016/j.meddos.2014.11.004] [PMID]
2. Xie Y, Bourgeois D, Guo B, Zhang R. Postmastectomy radiotherapy for left-sided breast cancer patients: Comparison of advanced techniques. Med Dosim 2020;45(1):34-40. [DOI:10.1016/j.meddos.2019.04.005] [PMID] [PMCID]
3. Cozzi L, Buffa FM, Fogliata A. Comparative analysis of dose volume histogram reduction algorithms for normal tissue complication probability calculations. Acta Oncol 2000;39(2):165-71. [DOI:10.1080/028418600430725] [PMID]
4. Bufacchi A, Nardiello B, Capparella R, Begnozzi L. Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters. Radiat Oncol 2013;8(1):164. [DOI:10.1186/1748-717X-8-164] [PMID] [PMCID]
5. Senthilkumar K, Das KM. Comparison of biological-based and dose volume-based intensity-modulated radiotherapy plans generated using the same treatment planning system. J Cancer Res Ther 2019;15(8):33. [DOI:10.4103/jcrt.JCRT_956_16] [PMID]
6. Li XA, Alber M, Deasy JO, Jackson A, Jee KWK, Marks LB, et al. The use and QA of biologically related models for treatment planning: Short report of the TG‐166 of the therapy physics committee of the AAPM. Med Phys 2012;39(3):1386-409. [DOI:10.1118/1.3685447] [PMID]
7. Taheri H, Tavakoli MB, Akhavan A. Radiobiological evaluation of three common clinical radiotherapy techniques including combined photon-electron, tangential beams and electron therapy in left-sided mastectomy patients. Adv Biomed Res 2018;7:99. [DOI:10.4103/abr.abr_198_17] [PMID] [PMCID]
8. Cella L, Palma G, Deasy JO, Oh JH, Liuzzi R, D'Avino V, et al. Complication probability models for radiation-induced heart valvular dysfunction: do heart-lung interactions play a role? PLoS One 2014;9(10):e111753. [DOI:10.1371/journal.pone.0111753] [PMID] [PMCID]
9. Chaikh A, Ojala J, Khamphan C, Garcia R, Giraud JY, Thariat J, et al. Dosimetrical and radiobiological approach to manage the dosimetric shift in the transition of dose calculation algorithm in radiation oncology: how to improve high quality treatment and avoid unexpected outcomes? Radiat Oncol 2018;13(1):60. [DOI:10.1186/s13014-018-1005-2] [PMID] [PMCID]
10. Petillion S, Swinnen A, Defraene G, Verhoeven K, Weltens C, den Heuvel FV. The photon dose calculation algorithm used in breast radiotherapy has significant impact on the parameters of radiobiological models. J Appl Clin Med Phys 2014;15(4):259-69. [DOI:10.1120/jacmp.v15i4.4853] [PMID] [PMCID]
11. Chaikh A, Khamphan C, Kumar T, Garcia R, Balosso J. What should we know about photon dose calculation algorithms used for radiotherapy? Their impact on dose distribution and medical decisions based on TCP/NTCP. Int J Cancer Ther Oncol 2016;4(4):4418. [Google Scholar]
12. Elcim Y, Dirican B, Yavas O. Dosimetric comparison of pencil beam and Monte Carlo algorithms in conformal lung radiotherapy. J Appl Clin Med Phys 2018;19(5):616-24. [DOI:10.1002/acm2.12426] [PMID] [PMCID]
13. Chaikh A, Docquière N, Bondiau P-Y, Balosso J. Impact of dose calculation models on radiotherapy outcomes and quality adjusted life years for lung cancer treatment: do we need to measure radiotherapy outcomes to tune the radiobiological parameters of a normal tissue complication probability model? Transl Lung Cancer Res 2016;5(6):673. [DOI:10.21037/tlcr.2016.11.04] [PMID] [PMCID]
14. Kaneko A, Sumida I, Mizuno H, Isohashi F, Suzuki O, Seo Y, et al. Comparison of gamma index based on dosimetric error and clinically relevant dose-volume index based on three-dimensional dose prediction in breast intensity-modulated radiation therapy. Radiat Oncol2019;14(1):36. [DOI:10.1186/s13014-019-1233-0] [PMID] [PMCID]
15. Chaikh A, Balosso J. NTCP variability in radiotherapy of lung cancer when changing the radiobiologic models and the photon dose calculation algorithms. J Cancer Clin Oncol 2016;2:100108. [DOI:10.1016/j.ejmp.2016.07.584]
16. Nielsen TB, Wieslander E, Fogliata A, Nielsen M, Hansen O, Brink C. Influence of dose calculation algorithms on the predicted dose distributions and NTCP values for NSCLC patients. Med Phys 2011;38(5):2412-8. [DOI:10.1118/1.3575418] [PMID]
17. Liang X, Penagaricano J, Zheng D, Morrill S, Zhang X, Corry P, et al. Radiobiological impact of dose calculation algorithms on biologically optimized IMRT lung stereotactic body radiation therapy plans. Radiat Oncol 2016;11(1):10. [DOI:10.1186/s13014-015-0578-2] [PMID] [PMCID]
18. Seppenwoolde Y, Lebesque JV, De Jaeger K, Belderbos JS, Boersma LJ, Schilstra C, et al. Comparing different NTCP models that predict the incidence of radiation pneumonitis. Int J Radiat Oncol Biol Phys 2003;55(3):724-35. [DOI:10.1016/S0360-3016(02)03986-X]
19. Gagliardi G, Constine LS, Moiseenko V, Correa C, Pierce LJ, Allen AM, et al. Radiation dose-volume effects in the heart. Int J Radiat Oncol Biol Phys 2010;76(3):S77-S85. [DOI:10.1016/j.ijrobp.2009.04.093] [PMID]
20. Okunieff P, Morgan D, Niemierko A, Suit HD. Radiation dose-response of human tumors. Int J Radiat Oncol Biol Phys 1995;32(4):1227-37. [DOI:10.1016/0360-3016(94)00475-Z]
21. Emami B, Lyman J, Brown A, Cola L, Goitein M, Munzenrider J, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 1991;21(1):109-22. [DOI:10.1016/0360-3016(91)90171-Y]
22. Burman C, Kutcher G, Emami B, Goitein M. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 1991;21(1):123-35. [DOI:10.1016/0360-3016(91)90172-Z]
23. Kwa SL, Lebesque JV, Theuws JC, Marks LB, Munley MT, Bentel G, et al. Radiation pneumonitis as a function of mean lung dose: an analysis of pooled data of 540 patients. Int J Radiat Oncol Biol Phys 1998;42(1):1-9. [DOI:10.1016/S0360-3016(98)00196-5]
24. Gagliardi G, Bjöhle J, Lax I, Ottolenghi A, Eriksson F, Liedberg A, et al. Radiation pneumonitis after breast cancer irradiation: analysis of the complication probability using the relative seriality model. Int J Radiat Oncol Biol Phys 2000;46(2):373-81. [DOI:10.1016/S0360-3016(99)00420-4]
25. Ågren Cronqvist A-K. Quantification of the response of heterogeneous tumours and organized normal tissues to fractionated radiotherapy. Stockholm University; 1995. [Google Book]
26. Martel MK, Sahijdak WM, Ten Haken RK, Kessler ML, Turrisi AT. Fraction size and dose parameters related to the incidence of pericardial effusions. Int J Radiat Oncol Biol Phys 1998;40(1):155-61. [DOI:10.1016/S0360-3016(97)00584-1]
27. Gagliardi G, Lax I, Ottolenghi A, Rutqvist L. Long-term cardiac mortality after radiotherapy of breast cancer-application of the relative seriality model. Br J Radiol 1996;69(825):839-46. [DOI:10.1259/0007-1285-69-825-839] [PMID]
28. Edvardsson A, Nilsson MP, Amptoulach S, Ceberg S. Comparison of doses and NTCP to risk organs with enhanced inspiration gating and free breathing for left-sided breast cancer radiotherapy using the AAA algorithm. Radiat Oncol 2015;10(1):84. [DOI:10.1186/s13014-015-0375-y] [PMID] [PMCID]
29. Rancati T, Wennberg B, Lind P, Svane G, Gagliardi G. Early clinical and radiological pulmonary complications following breast cancer radiation therapy: NTCP fit with four different models. Radiother Oncol 2007;82(3):308-16. [DOI:10.1016/j.radonc.2006.12.001] [PMID]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
