Monaco计划系统控制点统计不确定度选择对鼻咽癌剂量计算影响

doi:10.3760/cma.j.cn113030-20190113-00019

Abstract
Figure/Table
References()
Related Citation (15)
Read Tracking
Download Tracking

Download: PDF (0 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS) Supporting Info

Abstract Objective To explore the influence of the selection of statistical uncertainty of control points in Monaco planning system on the dose distribution of nasopharyngeal carcinoma (NPC), aiming to provide the statistical uncertainty of single control point in Monte Carlo calculation which satisfies clinical needs. Methods First, nine 10cm×10cm square fields with an equal interval of gantry angle were designed and five cases of 9-field intensity-modulated radiotherapy (IMRT) and five cases of single-arc volumetric-modulated arc therapy (VMAT) plans were randomly selected, Then, quality assurance (QA) verification plan using patient CT as QA phantom was created. Second, the grid spacing was selected as 3 mm during the calculation of dose distribution of QA plan. The statistical uncertainties of single control point were selected as 1%, 2%, 3%, 4% and 5%, respectively. Last, the deviation of dose distribution between different statistical uncertainties and 1% statistical uncertainty was analyzed. Results For a square field and single IMRT field, the dose deviation of center point was almost 7% while the statistical uncertainty was selected 4%. For 9-field IMRT and single-arc VMAT, the dose deviation of center point was ≤ 1.5% and the average dose deviation of PTV was ≤ 0.3% when the statistical uncertainty of control points was changed from 1% to 5%. The percentage of the point dose deviation of the coronary plane of ≤ 1% was greater than 99% when the statistical uncertainty was ≤ 3% for 9-filed IMRT and 4% for single-arc VMAT. Conclusions For the Monaco treatment planning system based on Monte Carlo calculation, the changes in the statistical uncertainty of control point from 1% to 5% exert significant effect upon the single field. In clinical application, the statistical uncertainty of control point should be ≤ 3% for 9-field IMRT and ≤ 4% for single-arc VMAT.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Wu Siyu
	Huang Xiaoyan
	Cao Wufei
	Chen Li

Key words： Monte Carlo system Uncertainty Nasopharyngeal neoplasm/intensity-modulated radiotherapy Nasopharyngeal neoplasm/volumetric-modulated arc therapy

Received: 13 January 2019

Corresponding Authors: Huang Xiaoyan, Email:huangxiaoy@sysucc.org.cn

Cite this article:

Wu Siyu,Huang Xiaoyan,Cao Wufei et al. Effect of selection of statistical uncertainty of control points in Monaco planning system on dose calculation in nasopharyngeal carcinoma[J]. Chinese Journal of Radiation Oncology, 2021, 30(2): 170-174.

URL:

http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.cn113030-20190113-00019 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2021/V30/I2/170

[1] Report of the AAPM Task Group No.105. Issues associated with clinical implementation of monte carlo-based photon and electron external beam treatment planning[J]. MedPhys, 2007, 34(12):4818-4853. DOI:10.1118/1.2795842.
[2] Qin N, Shen C, Tsai MY, et al. Full Monte Carlo-based biologic treatment plan optimization system for intensity modulated carbon ion therapy on graphics processing unit[J]. Int J Radiat Oncol Biol Phys, 2018, 100(1):235-243. DOI:10.1016/j.ijrobp.2017.09.002.
[3] Lee JY, Kim JK. A new propagation analysis of statistical uncertainty in multi-group cross sections generated by Monte Carlo method[J]. Ann Nucl Energy, 2018, 120(10):477-484. DOI:10.1016/j.anucene.2018.06.004.
[4] Ma CM, Li JS, Jiang SB, et al. Effect of statistical uncertainties on Monte Carlo treatment planning[J]. Phys Med Biol, 2005, 50(5):901-907. DOI:10.1088/0031-9155/50/5/013.
[5] Chetty IJ, Rosu M, Kessler ml, et al. Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning[J]. Int J Radiat Oncol Biol Phys, 2006, 65(4):1249-1259. DOI:10.1016/j.ijrobp.2006.03.039.
[6] Fotina I, Winkler P, Künzler T, et al. Advanced kernel methods vs. Monte Carlo-based dose calculation for high energy photon beams[J]. Radiother Oncol, 2009, 93(3):645-653. DOI:10.1016/j.radonc.2009.10.013.
[6] Boggula R, Jahnke L, Wertz H, et al. Patient-specific 3D pretreatment and potential 3D online dose verification of Monte Carlo-calculated IMRT prostate treatment plans[J]. Int J Radiat Oncol Biol Phys, 2011, 81(4):1168-1175. DOI:10.1016/j.ijrobp.2010.09.010.
[7] 樊林,肖明勇,傅玉川. 不同剂量计算算法在临床肺癌调强计划的体积-剂量值差异研究[J]. 西部医学,2016,28(6):800-810. DOI:10.3969/j.issn.1672-3511.2016.06.014.
Fan L, Xiao MY, Fu YC. Study on the difference of volume dose value of different dose calculation algorithms in intensity-modulated lung cancer[J]. Med J West China, 2016, 28(6):800-810. DOI:10.3969/j.issn.1672-3511.2016.06.014.
[8] Narayanasamy G, Saenz DL, Defoor D, et al. Dosimetric validation of Monaco treatment planning system on an Elekta Versa HD linear accelerator[J]. J Appl Clin Med Phys, 2017, 18(6):123-129. DOI:10.1002/acm2.12188.
[9] 杨耕,王学涛,张白霖,等. 基于蒙特卡罗模拟的调强放疗计划剂量验证的应用研究[J]. 中华放射医学与防护杂志,2017,37(5):384-388. DOI:10.3760/cma.j.issn.0254-5098.2017.05.013.
Yang G, Wang XT, Zhang BL, et al. Study on the application of dose verification of IMRT plan based on Monte Carlo simulation[J]. Chin J Radiol Med Propect, 2017, 37(5):384-388. DOI:10.3760/cma.j.issn.0254-5098.2017.05.013.
[10] Depuydt T, Van Esch A, Huyskens DP. A quantitative evaluation of IMRT dose distributions:refinement and clinical assessment of the gamma evaluation[J]. Radiother Oncol, 2002, 62(3):309-319. DOI:10.1016/S0167-8140(01)00497-2.
[11] 郭跃信,王海洋,刘乐乐,等. 早期非小细胞肺癌不同放疗技术的剂量学差异分析[J]. 中华放射肿瘤学杂志,2017,26(1):62-65. DOI:10.3760/cma.j.issn.1004-4221.2017.01.014.
Guo YX, Wang HY, Liu LL, et al. Dosimetric analysis of different radiotherapy techniques for early non-small cell lung cancer[J]. Chin J Radiat Oncol, 2017, 26(1):62-65. DOI:10.3760/cma.j.issn.1004-4221.2017.01.014.
[12] 唐斌,康盛伟,王先良,等. 基于直线加速器虚拟源模型的蒙特卡洛剂量计算及在IMRT独立验算中的初步应用[J]. 中华放射肿瘤学杂志,2016,25(4):372-375. DOI:10.3760/cma.j.issn.1004-4221.2016.04.014.
Tang B, Kang SW, Wang XL, et al. Monte Carlo dose calculation based on virtual source model of linac and its preliminary application in IMRT independent calculation[J]. Chin J Radiat Oncol, 2016, 25(4):372-375. DOI:10.3760/cma.j.issn.1004-4221.2016.04.014.
[13] Tian Z, Jia X, Graves Y, et al. SU-E-T-503:IMRT optimization using Monte Carlo dose engine:The effect of statistical uncertainty[J]. Med Phys, 2012, 39(6 Part 18):3821-3821. DOI:10.1118/1.4735592.