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蒙特卡罗算法和笔形束算法在乳腺癌调强质子治疗中的综合剂量学研究。

A comprehensive dosimetric study of Monte Carlo and pencil-beam algorithms on intensity-modulated proton therapy for breast cancer.

作者信息

Liang Xiaoying, Li Zuofeng, Zheng Dandan, Bradley Julie A, Rutenberg Michael, Mendenhall Nancy

机构信息

Department of Radiation Oncology, University of Florida College of Medicine, Gainesville, FL, USA.

Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA.

出版信息

J Appl Clin Med Phys. 2019 Jan;20(1):128-136. doi: 10.1002/acm2.12497. Epub 2018 Nov 28.

Abstract

PB algorithms are commonly used for proton therapy. Previously reported limitations of the PB algorithm for proton therapy are mainly focused on high-density gradients and small-field dosimetry, the effect of PB algorithms on intensity-modulated proton therapy (IMPT) for breast cancer has yet to be illuminated. In this study, we examined 20 patients with breast cancer and systematically investigated the dosimetric impact of MC and PB algorithms on IMPT. Four plans were generated for each patient: (a) a PB plan that optimized and computed the final dose using a PB algorithm; (b) a MC-recomputed plan that recomputed the final dose of the PB plan using a MC algorithm; (c) a MC-renormalized plan that renormalized the MC-recomputed plan to restore the target coverage; and (d) a MC-optimized plan that optimized and computed the final dose using a MC algorithm. The DVH on CTVs and on organ-at-risks (OARs) from each plan were studied. The Mann-Whitney U-test was used for testing the differences between any two types of plans. We found that PB algorithms significantly overestimated the target dose in breast IMPT plans. The median value of the CTV D , D , and D dropped by 3.7%, 3.4%, and 2.1%, respectively, of the prescription dose in the MC-recomputed plans compared with the PB plans. The magnitude of the target dose overestimation by the PB algorithm was higher for the breast CTV than for the chest wall CTV. In the MC-renormalized plans, the target dose coverage was comparable with the original PB plans, but renormalization led to a significant increase in target hot spots as well as skin dose. The MC-optimized plans led to sufficient target dose coverage, acceptable target hot spots, and good sparing of skin and other OARs. Utilizing the MC algorithm for both plan optimization and final dose computation in breast IMPT treatment planning is therefore desirable.

摘要

铅笔束(PB)算法常用于质子治疗。先前报道的质子治疗PB算法的局限性主要集中在高密度梯度和小射野剂量学方面,PB算法对乳腺癌调强质子治疗(IMPT)的影响尚未阐明。在本研究中,我们检查了20例乳腺癌患者,并系统地研究了蒙特卡罗(MC)算法和PB算法对IMPT的剂量学影响。为每位患者生成了四个计划:(a)使用PB算法优化并计算最终剂量的PB计划;(b)使用MC算法重新计算PB计划最终剂量的MC重新计算计划;(c)对MC重新计算计划进行归一化以恢复靶区覆盖的MC归一化计划;(d)使用MC算法优化并计算最终剂量的MC优化计划。研究了每个计划在临床靶区(CTV)和危及器官(OAR)上的剂量体积直方图(DVH)。采用曼-惠特尼U检验来检验任意两种计划之间的差异。我们发现,PB算法在乳腺癌IMPT计划中显著高估了靶区剂量。与PB计划相比,MC重新计算计划中CTV的Dmean、D95和D99的中位数分别下降了处方剂量的3.7%、3.4%和2.1%。PB算法对乳腺癌CTV的靶区剂量高估幅度高于胸壁CTV。在MC归一化计划中,靶区剂量覆盖与原始PB计划相当,但归一化导致靶区热点以及皮肤剂量显著增加。MC优化计划导致了足够 的靶区剂量覆盖、可接受的靶区热点以及对皮肤和其他OAR的良好保护。因此,在乳腺癌IMPT治疗计划中,使用MC算法进行计划优化和最终剂量计算是可取的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d900/6333133/c340af277955/ACM2-20-128-g001.jpg

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