AbstractObjective To validate the accuracy of physical model of in-vivo 3D dose verification based on electronic portal imaging device (EPID) using the phantom and preliminarily analyze the clinical application. Methods Two phantoms (uniform and non-uniform phantoms) were involved in this study. The system of in-vivo 3D dose verification based on EPID was employed to acquire the images of square fields (SF) and combined fields of intensity-modulated radiotherapy (CFIMRT). The physical model of different media was constructed using the system. The factor of γ passing rate under different dose/distance criteria was statistically compared. For clinical cases, the dose-volume histograms were adopted to analyze the dose distribution of target volume and organs at risk (OARs). Results For the SF in the uniform phantom, the average γ passing rate (3%/3mm) was (97.49±1.11)%, and (94.06±5.11)% for the SF in the non-uniform phantom (P>0.05). No statistical significance was noted in IMRT using different delivery methods (all P>0.05). For clinical cases, the average γ passing rate (3%/2mm) was (97.96±1.84)% in the pre-treatment dose verification, and (90.51±6.96)%(3%/3mm) for the in-vivo 3D dose verification. For clinical cases, significant dose deviation was observed in OARs with small size and large volume changes. Conclusion The in-vivo 3D dose verification model based on EPID can be effectively applied in inter-fraction dose verification, providing technical support for adaptive radiotherapy in clinical practice.
Fund:Science and Technology Project of Henan Province (162102310321)
Corresponding Authors:
Lei Hongchang, Email:leihongchang@live.com
Cite this article:
Mao Ronghu,Guo Wei,Li Bing et al. Preliminary study of physical model test and clinical application based on EPID-based in-vivo dose verification system[J]. Chinese Journal of Radiation Oncology, 2021, 30(10): 1065-1070.
Mao Ronghu,Guo Wei,Li Bing et al. Preliminary study of physical model test and clinical application based on EPID-based in-vivo dose verification system[J]. Chinese Journal of Radiation Oncology, 2021, 30(10): 1065-1070.
[1] Stambaugh C, Ezzell G. A clinically relevant IMRT QA workflow:design and validation[J]. Med Phys, 2018, 45(4):1391-1399. DOI:10.1002/mp.12838. [2] Esposito M, Villaggi E, Bresciani S, et al. Estimating dose delivery accuracy in stereotactic body radiation therapy:A review of in-vivo measurement methods[J]. Radiother Oncol, 2020, 149:158-167. DOI:10.1016/j.radonc.2020.05.014. [3] Miften M, Olch A, Mihailidis D, et al. Tolerance limits and methodologies for IMRT measurement-based verification QA:recommendations of AAPM Task Group No. 218[J]. Med Phys, 2018, 45(4):e53-e83. DOI:10.1002/mp.12810. [4] Zhu J, Chen L, Chen A, et al. Fast 3D dosimetric verifications based on an electronic portal imaging device using a GPU calculation engine[J]. Radiat Oncol, 2015, 10:85. DOI:10.1186/s13014-015-0387-7. [5] Li Y, Zhu J, Shi J, et al. Investigating the effectiveness of monitoring relevant variations during IMRT and VMAT treatments by EPID-based 3D in vivo verification performed using planning CTs[J]. PLoS One, 2019, 14(6):e0218803. DOI:10.1371/journal.pone.0218803. [6] Coleman L, Skourou C. Sensitivity of volumetric modulated arc therapy patient specific QA results to multileaf collimator errors and correlation to dose volume histogram based metrics[J]. Med Phys, 2013, 40(11):1117151-1117157. DOI:10.1118/1.4824433. [7] Chan MF, Li J, Schupak K, et al. Using a novel dose QA tool to quantify the impact of systematic errors otherwise undetected by conventional QA methods:clinical head and neck case studies[J]. Technol Cancer Res Treat, 2014, 13(1):57-67. DOI:10.7785/tcrt.2012.500353. [8] 黄妙云,陈明秋,陈远贵,等. 基于EPID三维剂量验证系统的物理模型测试及临床应用的初步研究[J]. 中华放射肿瘤学杂志,2016, 25(12):1335-1340. DOI:10.3760/cma.j.issn.1004-4221.2016.12.014. Huang MY, Chen MQ, Chen YG, et al. A preliminary study of test and clinical application of a physical model based on the three-dimensional dose verification system using electronic portal imaging device[J]. Chin J Radiat Oncol, 2016, 25(12):1335-1340. DOI:10.3760/cma.j.issn.1004-4221.2016.12.014. [9] Wieslander E, Knöös T. Dose perturbation in the presence of metallic implants:treatment planning system versus monte carlo simulations[J]. Phys Med Biol, 2003, 48(20):3295-3305. DOI:10.1088/0031-9155/48/20/003. [10] Rozendaal RA, Mijnheer BJ, Hamming-Vrieze O, et al. Impact of daily anatomical changes on EPID-based in vivo dosimetry of VMAT treatments of head-and-neck cancer[J]. Radiother Oncol, 2015, 116(1):70-74. DOI:10.1016/j.radonc.2015.05.020. [11] Yedekci Y, Biltekin F, Ozyigit G, et al. Feasibility study of an electronic portal imaging based in vivo dose verification system for prostate stereotactic body radiotherapy[J]. Phys Med, 2019, 64:204-209. DOI:10.1016/j.ejmp. 2019.07.008. [12] Wang T, Lei Y, Manohar N, et al. Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy[J]. Med Dosim, 2019, 44(4):e71-e79. DOI:10.1016/j.meddos.2019.03.001. [13] Kurz C, Maspero M, Savenije MHF, et al. CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation[J]. Phys Med Biol, 2019, 64(22):225004. DOI:10.1088/1361-6560/ab4d8c. [14] van Rooijen DC, van Wieringen N, Stippel G, et al. Dose-guided radiotherapy:potential benefit of online dose recalculation for stereotactic lung irradiation in patients with non-small-cell lung cancer[J]. Int J Radiat Oncol Biol Phys, 2012, 83(4):e557-562. DOI:10.1016/j.ijrobp.2011.12.055. [15] Zegers Cml, Baeza JA, van Elmpt W, et al. Three-dimensional dose evaluation in breast cancer patients to define decision criteria for adaptive radiotherapy[J]. Acta Oncol, 2017, 56(11):1487-1494. DOI:10.1080/0284186X.2017.1349334.
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