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本文引用的文献

1
Mixed integer programming with dose-volume constraints in intensity-modulated proton therapy.调强质子治疗中具有剂量体积约束的混合整数规划
J Appl Clin Med Phys. 2017 Sep;18(5):29-35. doi: 10.1002/acm2.12130. Epub 2017 Jul 6.
2
Limited Impact of Setup and Range Uncertainties, Breathing Motion, and Interplay Effects in Robustly Optimized Intensity Modulated Proton Therapy for Stage III Non-small Cell Lung Cancer.在针对III期非小细胞肺癌的稳健优化调强质子治疗中,设置和范围不确定性、呼吸运动及相互作用效应的影响有限
Int J Radiat Oncol Biol Phys. 2016 Nov 1;96(3):661-9. doi: 10.1016/j.ijrobp.2016.06.2454. Epub 2016 Jun 29.
3
A Greedy reassignment algorithm for the PBS minimum monitor unit constraint.一种用于质子束治疗计划中最小监测单元约束的贪婪重新分配算法。
Phys Med Biol. 2016 Jun 21;61(12):4665-78. doi: 10.1088/0031-9155/61/12/4665. Epub 2016 Jun 1.
4
Robustness quantification methods comparison in volumetric modulated arc therapy to treat head and neck cancer.容积调强弧形放疗治疗头颈癌中稳健性量化方法的比较
Pract Radiat Oncol. 2016 Nov-Dec;6(6):e269-e275. doi: 10.1016/j.prro.2016.02.002. Epub 2016 Feb 13.
5
Exploratory Study of 4D versus 3D Robust Optimization in Intensity Modulated Proton Therapy for Lung Cancer.肺癌调强质子治疗中4D与3D稳健优化的探索性研究
Int J Radiat Oncol Biol Phys. 2016 May 1;95(1):523-533. doi: 10.1016/j.ijrobp.2015.11.002. Epub 2015 Nov 10.
6
Maximizing the probability of satisfying the clinical goals in radiation therapy treatment planning under setup uncertainty.在存在摆位不确定性的情况下,最大化放射治疗治疗计划中实现临床目标的概率。
Med Phys. 2015 Jul;42(7):3992-9. doi: 10.1118/1.4921998.
7
Robust optimization in intensity-modulated proton therapy to account for anatomy changes in lung cancer patients.用于考虑肺癌患者解剖结构变化的调强质子治疗中的稳健优化。
Radiother Oncol. 2015 Mar;114(3):367-72. doi: 10.1016/j.radonc.2015.01.017. Epub 2015 Feb 20.
8
A comparison of the dose distributions from three proton treatment planning systems in the planning of meningioma patients with single-field uniform dose pencil beam scanning.三种质子治疗计划系统在单野均匀剂量笔形束扫描的脑膜瘤患者计划中的剂量分布比较。
J Appl Clin Med Phys. 2015 Jan 8;16(1):4996. doi: 10.1120/jacmp.v16i1.4996.
9
Robustness of target dose coverage to motion uncertainties for scanned carbon ion beam tracking therapy of moving tumors.移动肿瘤的扫描碳离子束跟踪治疗中目标剂量覆盖对运动不确定性的稳健性。
Phys Med Biol. 2015 Feb 21;60(4):1717-40. doi: 10.1088/0031-9155/60/4/1717. Epub 2015 Feb 4.
10
Impact of respiratory motion on worst-case scenario optimized intensity modulated proton therapy for lung cancers.呼吸运动对肺癌最坏情况优化调强质子治疗的影响。
Pract Radiat Oncol. 2015 Mar-Apr;5(2):e77-86. doi: 10.1016/j.prro.2014.08.002. Epub 2014 Sep 11.

在调强质子治疗中使用二次目标函数进行稳健优化以考虑最小监测单位约束。

Robust optimization in IMPT using quadratic objective functions to account for the minimum MU constraint.

作者信息

Shan Jie, An Yu, Bues Martin, Schild Steven E, Liu Wei

机构信息

Department of Biomedical Informatics, Arizona State University, Tempe, AZ, USA.

Department of Radiation Oncology, Mayo Clinic Hospital, Phoenix, AZ, USA.

出版信息

Med Phys. 2018 Jan;45(1):460-469. doi: 10.1002/mp.12677. Epub 2017 Dec 5.

DOI:10.1002/mp.12677
PMID:29148570
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5774242/
Abstract

PURPOSE

Currently, in clinical practice of intensity-modulated proton therapy (IMPT), the influence of the minimum monitor unit (MU) constraint is taken into account through postprocessing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization.

METHODS AND MATERIALS

Cao et al. have demonstrated a two-stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using seven cancer patients with different machine settings.

RESULT

The new method achieved better plan quality than the conventional method. The D of the clinical target volume (CTV) normalized to the prescription dose was (mean [min-max]): (99.4% [99.2%-99.6%]) vs. (99.2% [98.6%-99.6%]). Plan robustness derived from these two methods was comparable. For all seven patients, the CTV dose-volume histogram band gap (narrower band gap means more robust plans) at D normalized to the prescription dose was (mean [min-max]): (1.5% [0.5%-4.3%]) vs. (1.2% [0.6%-3.8%]).

CONCLUSION

Our new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine-deliverable plans with better tumor coverage while maintaining high-plan robustness.

摘要

目的

目前,在调强质子治疗(IMPT)的临床实践中,最小监测单位(MU)约束的影响是在优化完成后通过后处理来考虑的。这可能会降低计划质量和计划稳健性。本研究旨在在计划稳健优化过程中直接减轻最小MU约束的影响。

方法和材料

Cao等人展示了一种两阶段方法,用于在不考虑不确定性影响的情况下使用线性规划来考虑最小MU约束。在本研究中,我们使用二次优化来考虑最小MU约束,并同时使用稳健优化来考虑不确定性的影响。我们使用七名具有不同机器设置的癌症患者评估了我们的方法。

结果

新方法比传统方法实现了更好的计划质量。临床靶体积(CTV)归一化到处方剂量的D值为(平均值[最小值 - 最大值]):(99.4% [99.2% - 99.6%])对(99.2% [98.6% - 99.6%])。这两种方法得出的计划稳健性相当。对于所有七名患者,CTV剂量体积直方图带隙(带隙越窄意味着计划越稳健)在归一化到处方剂量的D值处为(平均值[最小值 - 最大值]):(1.5% [0.5% - 4.3%])对(1.2% [0.6% - 3.8%])。

结论

我们将最小MU约束直接纳入计划稳健优化的新方法可以生成机器可交付的计划,在保持高计划稳健性的同时具有更好的肿瘤覆盖。