PDF
Abstract
Incorporation of the Monte Carlo (MC) algorithm in optimizing CyberKnife (CK) plans is cumbersome, and early models unconfgured MC calculations, therefore, this study investigated algorithm-based dose calculation discrepancies by selecting different prescription isodose lines (PIDLs) in head and lung CK plans. CK plans were based on anthropomorphic phantoms. Four shells were set at 2–60 mm from the target, and the constraint doses were adjusted according to the design strategy. After optimization, 30%–90% PIDL plans were generated by ray tracing (RT). In the evaluation module, CK plans were recalculated using the MC algorithm. Therefore, the dosimetric parameters of different PIDL plans based on the RT and MC algorithms were obtained and analyzed. The discrepancies (mean±SD) were 3.72%±0.31%, 3.40%±0.11%, 3.47%±0.32%, 0.17%±0.11%, 0.64%±3.60%, 7.73%±1.60%, 14.62%±3.21% and 10.10%±1.57% for D1%, D(mean), D98% and coverage of the PTV, DGI, V5, V3 and V1 in the head plans and −6.32%±1.15%, −13.46%±0.98%, −20.63%±2.25%, −34.78%±25.03%, 122.48%±175.60%, −12.92%±5.41%, 3.19%±4.67% and 7.13%±1.56% in the lung plans, respectively. The following parameters were signifcantly correlated with PIDL: dD98% at the 0.05 level and dDGI, dV5 and dV3 at the 0.01 level for the head plans; dD98% at the 0.05 level and dD1%, dD(mean), dCoverage, dDGI, dV5 and dV3 at the 0.01 level for the lung plans. RT may be used to calculate the dose in CK head plans, but when the dose of organs at risk is close to the limit, it is necessary to refer to the MC results or to further optimize the CK plan to reduce the dose. For lung plans, the MC algorithm is recommended. For early models without the MC algorithm, a lower PIDL plan is recommended; otherwise, a large PIDL plan risks serious underdosage in the target area.
Keywords
CyberKnife
/
prescription isodose line
/
Monte Carlo
/
ray tracing
/
phantom
Cite this article
Download citation ▾
Jing Yang, Gang Liu, Hong-yuan Liu, Xin Nie, Zhi-yong Yang, Jun Han, Sheng Zhang, Zhi-wen Liang.
Influence of CyberKnife Prescription Isodose Line on the Discrepancy of Dose Results Calculated by the Ray Tracing and Monte Carlo Algorithms for Head and Lung Plans: A Phantom Study.
Current Medical Science, 2020, 40(2): 301-306 DOI:10.1007/s11596-020-2177-1
| [1] |
OnishiH, ShiratoH, NagataY, et al.. Stereotactic Body Radiotherapy (SBRT) for Operable Stage I Non- Small-Cell Lung Cancer: Can SBRT Be Comparable to Surgery?. Int J Radiat Oncol Biol Phys, 2011, 81(5): 1352-1358
|
| [2] |
OkoyeCC, PatelRB, HasanS, et al.. Comparison of Ray Tracing and Monte Carlo Calculation Algorithms for Thoracic Spine Lesions Treated With CyberKnife-Based Stereotactic Body Radiation Therapy. Technol Cancer Res Treat, 2016, 15(1): 196-202
|
| [3] |
IiiRLE, HiattJ, AdetokunboO, et al.. Dosimetric Accuracy of Ray-tracing Algorithm for Treatment of Thoracic Spine Lesions Using Robotic Radiosurgery. Int J Radiat Oncol Biol Phys, 2012, 84(3): S280-S280
|
| [4] |
GalonskeK, ThieleM, ErnstI, et al.. Comparison of treatment plans calculated by Ray Tracing and Monte Carlo algorithms for head and thorax radiotherapy with Cyberknife. Curr Direct Biomed Eng, 2017, 3(2): 647-650
|
| [5] |
van der Voort van ZypNC, HoogemanMS, Steven van de Water, et al.. Clinical introduction of Monte Carlo treatment planning: a different prescription dose for non-small cell lung cancer according to tumor location and size. Radiother Oncol, 2010, 96(1): 55-60
|
| [6] |
WuVWC, TamKW, TongSM. Evaluation of the influence of tumor location and size on the difference of dose calculation between Ray Tracing algorithm and Fast Monte Carlo algorithm in stereotactic body radiotherapy of non-small cell lung cancer using CyberKnife. J Appl Clin Med Phys, 2013, 14(5): 68-78
|
| [7] |
YangJ, LiuH, CaoT, et al.. Effect of the discrepancy of the dose calculation results of different algorithms on CyberKnife lung tumor treatment plan. Chin J Radiat Oncol, 2018, 27(12): 1083-1087
|
| [8] |
WilcoxEE, DaskalovGM, LincolnH, et al.. Comparison of planned dose distributions calculated by Monte Carlo and Ray-Trace algorithms for the treatment of lung tumors with cyberknife: a preliminary study in 33 patients. Int J Radiat Oncol Biol Phys, 2010, 77(1): 277-284
|
| [9] |
XuQ, FanJ, GrimmJ, et al.. The dosimetric impact of the prescription isodose line (IDL) on the quality of robotic stereotactic radiosurgery (SRS) plans. Med Phys, 2017, 44(12): 6159-6165
|
| [10] |
CaoY, ZhuX, JuX, et al.. Optimization of dose distributions of target volumes and organs at risk during stereotactic body radiation therapy for pancreatic cancer with dose-limiting auto-shells. Radiat Oncol, 2018, 13(11): 1-6
|
| [11] |
LeeSW, JangS, PyakuryalAP, et al.. The impact of CyberKnife’s prescription isodose percentage on intracranial target planning. J Appl Clin Med Phys, 2014, 15(5): 278-280
|
| [12] |
PaddickI, LippitzB. A simple dose gradient measurement tool to complement the conformity index. J Neurosurg, 2006, 105: 194-201
|
| [13] |
WilcoxEE, DaskalovGM. Accuracy of dose measurements and calculations within and beyond heterogeneous tissues for 6 MV photon fields smaller than 4 cm produced by Cyberknife. Med Phys, 2008, 35(6): 2259-2266
|
| [14] |
SolbergTD, DemarcoJJ, HollyFE, et al.. Monte Carlo treatment planning for stereotactic radiosurgery. Radiot Oncol J Euro Soc Therap Radiol Oncol, 1998, 49(1): 73-84
|
| [15] |
KoksalC, AkbasU, OkutanM, et al.. Comparison of dose distributions calculated by the cyberknife Monte Carlo and ray tracing algorithms for lung tumors: a phantom study, 2015
|
| [16] |
ArakiF. Monte Carlo study of a Cyberknife stereotactic radiosurgery system. Med Phys, 2006, 33(8): 2955-2963
|
| [17] |
PanY, YangR, LiJ, et al.. Film-based dose validation of Monte Carlo algorithm for Cyberknife system with a CIRS thorax phantom. J Appl Clin Med Phys, 2018, 19(3): 142-148
|
| [18] |
VaiA, BonfantiP, InvernizziM, et al.. GTV-based prescription and Monte Carlo treatment planning in Cyberknife treatments for lung lesions. Radiother Oncol, 2016, 119: S408-S409
|
| [19] |
JinL, WangL, LiJ, et al.. Investigation of optimal beam margins for stereotactic radiotherapy of lung-cancer using Monte Carlo dose calculations. Phys Med Biol, 2007, 52(12): 3549-3561
|
| [20] |
AltmanMB, JinJY, KimS, et al.. Practical methods for improving dose distributions in Monte Carlo-based IMRT planning of lung wall-seated tumors treated with SBRT. J Appl Clin Med Phys, 2012, 13(6): 112-125
|
| [21] |
SahgalA, BaraniIJ, BaraniNJJr, et al.. Prescription dose guideline based on physical criterion for multiple metastatic brain tumors treated with stereotactic radiosurgery. Int J Radiat Oncol Biol Phys, 2010, 78(2): 605-608
|
| [22] |
PettiPL, ChuangCF, SmithV, et al.. Peripheral doses in CyberKnife radiosurgery. Med Phys, 2006, 33(6): 1770-1779
|
| [23] |
SioTT, JangS, LeeSW, et al.. Comparing gamma knife and cyberknife in patients with brain metastases. J Appl Clin Med Phys, 2014, 15(1): 14-26
|