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Int J Radiat Oncol Biol Phys. 2016 Aug 1;95(5):1535-1543. doi: 10.1016/j.ijrobp.2016.03.041. Epub 2016 Apr 6.
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Phys Med Biol. 2015 Nov 7;60(21):8399-416. doi: 10.1088/0031-9155/60/21/8399. Epub 2015 Oct 13.
3
Spatial mapping of the biologic effectiveness of scanned particle beams: towards biologically optimized particle therapy.扫描粒子束生物有效性的空间映射:迈向生物优化粒子治疗
Sci Rep. 2015 May 18;5:9850. doi: 10.1038/srep09850.
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Int J Radiat Oncol Biol Phys. 2015 Apr 1;91(5):1057-64. doi: 10.1016/j.ijrobp.2014.12.049.
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Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study.基于图形处理器(GPU)的质子放射治疗蒙特卡罗代码(gPMC)的验证:临床病例研究
Phys Med Biol. 2015 Mar 21;60(6):2257-69. doi: 10.1088/0031-9155/60/6/2257. Epub 2015 Feb 26.
6
Relative biological effectiveness (RBE) values for proton beam therapy. Variations as a function of biological endpoint, dose, and linear energy transfer.质子束治疗的相对生物效应(RBE)值。作为生物终点、剂量和线能量转移函数的变化。
Phys Med Biol. 2014 Nov 21;59(22):R419-72. doi: 10.1088/0031-9155/59/22/R419. Epub 2014 Oct 31.
7
Linear energy transfer-guided optimization in intensity modulated proton therapy: feasibility study and clinical potential.线性能量传递引导的调强质子治疗优化:可行性研究和临床潜力。
Int J Radiat Oncol Biol Phys. 2013 Sep 1;87(1):216-22. doi: 10.1016/j.ijrobp.2013.05.013. Epub 2013 Jun 19.
8
GPU-based fast Monte Carlo dose calculation for proton therapy.基于 GPU 的质子治疗快速蒙特卡罗剂量计算。
Phys Med Biol. 2012 Dec 7;57(23):7783-97. doi: 10.1088/0031-9155/57/23/7783. Epub 2012 Nov 6.
9
A model for the relative biological effectiveness of protons: the tissue specific parameter α/β of photons is a predictor for the sensitivity to LET changes.质子相对生物学效应模型:组织特异性参数α/β与光子相同,是预测对 LET 变化敏感性的指标。
Acta Oncol. 2013 Apr;52(3):580-8. doi: 10.3109/0284186X.2012.705892. Epub 2012 Aug 22.
10
Variations in linear energy transfer within clinical proton therapy fields and the potential for biological treatment planning.临床质子治疗场中线性能量转移的变化及其在生物学治疗计划中的潜在应用。
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基于线性能量传递的调强质子治疗计划的重新优化

Reoptimization of Intensity Modulated Proton Therapy Plans Based on Linear Energy Transfer.

作者信息

Unkelbach Jan, Botas Pablo, Giantsoudi Drosoula, Gorissen Bram L, Paganetti Harald

机构信息

Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts.

Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts; Faculty of Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany.

出版信息

Int J Radiat Oncol Biol Phys. 2016 Dec 1;96(5):1097-1106. doi: 10.1016/j.ijrobp.2016.08.038. Epub 2016 Sep 1.

DOI:10.1016/j.ijrobp.2016.08.038
PMID:27869082
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5133459/
Abstract

PURPOSE

We describe a treatment plan optimization method for intensity modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume while limiting degradation of the best possible physical dose distribution.

METHODS AND MATERIALS

To allow fast optimization based on dose and LET, a GPU-based Monte Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LET×D). To first approximation, LET×D represents a measure of the additional biological dose that is caused by high LET.

RESULTS

The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intracranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) in whom the target volume overlaps with the brainstem and optic structures. In all cases, high LET×D in critical structures could be avoided while minimally compromising physical dose planning objectives.

CONCLUSION

LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and relative biological effectiveness (RBE)-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects resulting from elevated RBE of proton beams near the end of range.

摘要

目的

我们描述了一种调强质子治疗(IMPT)的治疗计划优化方法,该方法可避免在靶区内或靶区附近的关键结构中出现高线性能量转移(LET)值,同时限制最佳物理剂量分布的退化。

方法和材料

为了基于剂量和LET进行快速优化,扩展了基于图形处理器(GPU)的蒙特卡罗代码,以便除了为所有笔形束提供剂量外,还能提供剂量平均LET。在基于物理剂量优化初始IMPT计划后,使用优先优化方案来修改LET分布,同时将物理剂量目标限制在接近初始计划的值。LET优化步骤基于对LET与物理剂量的乘积(LET×D)评估的目标函数来执行。初步近似来看,LET×D代表了由高LET引起的额外生物剂量的一种度量。

结果

该方法对于靶区内或靶区附近存在具有最大剂量约束的串联关键结构的治疗有效。我们报告了5例颅内肿瘤(高级别脑膜瘤、颅底脊索瘤、室管膜瘤)患者,其靶区与脑干和视觉结构重叠。在所有病例中,均可避免关键结构中出现高LET×D,同时对物理剂量规划目标的影响最小。

结论

基于LET的IMPT计划重新优化是一种务实的方法,可弥合纯基于物理剂量的计划与基于相对生物效应(RBE)的计划之间的差距。该方法通过降低质子束在射程末端附近RBE升高导致的潜在副作用增加风险,使IMPT治疗更安全。