Suppr超能文献

调强质子治疗中通过分布高线性转移能来重新分配头颈部肿瘤附近危及器官的分布。

Robust Optimization for Intensity Modulated Proton Therapy to Redistribute High Linear Energy Transfer from Nearby Critical Organs to Tumors in Head and Neck Cancer.

机构信息

Department of Radiation Oncology, Mayo Clinic in Arizona, Phoenix, Arizona.

Department of Radiation Oncology, Mayo Clinic in Arizona, Phoenix, Arizona.

出版信息

Int J Radiat Oncol Biol Phys. 2020 May 1;107(1):181-193. doi: 10.1016/j.ijrobp.2020.01.013. Epub 2020 Jan 25.

Abstract

PURPOSE

We propose linear energy transfer (LET)-guided robust optimization in intensity modulated proton therapy for head and neck cancer. This method simultaneously considers LET and physical dose distributions of tumors and organs at risk (OARs) with uncertainties.

METHODS AND MATERIALS

Fourteen patients with head and neck cancer were included in this retrospective study. Cord, brain stem, brain, and oral cavity were considered. Two algorithms, voxel-wise worst-case robust optimization and LET-guided robust optimization (LETRO), were used to generate intensity modulated proton therapy plans for each patient. The latter method directly optimized LET distributions rather than indirectly as in previous methods. LET-volume histograms (LETVHs) were generated, and high LET was redistributed from nearby OARs to tumors in a user-defined way via LET-volume constraints. Dose-volume histogram indices, such as clinical target volume (CTV) D and D-D, cord D, brain stem D, brain D, and oral cavity D, were calculated. Plan robustness was quantified using the worst-case analysis method. LETVH indices analogous to dose-volume histogram indices were used to characterize LET distributions. The Wilcoxon signed rank test was performed to measure statistical significance.

RESULTS

In the nominal scenario, LETRO provided higher LET distributions in the CTV (unit: keV/μm; CTV LET: 1.18 vs 1.08, LETRO vs RO, P = .0031) while preserving comparable physical dose and plan robustness. LETRO achieved significantly reduced LET distributions in the cord, brain stem, and oral cavity compared with RO (cord LET: 7.20 vs 8.20, P = .0010; brain stem LET: 10.95 vs 12.05, P = .0007; oral cavity LET: 2.11 vs 3.12, P = .0052) and had comparable physical dose and plan robustness in all OARs. In the worst-case scenario, LETRO achieved significantly higher LET in the CTV, reduced LET in the brain, and was comparable to other LETVH indices (CTV LET: 3.26 vs 3.35, P = .0012; brain LET: 24.80 vs 22.00, P = .0016).

CONCLUSIONS

LETRO robustly optimized LET and physical dose distributions simultaneously. It redistributed high LET from OARs to targets with slightly modified physical dose and plan robustness.

摘要

目的

我们提出了一种基于线性能量传递(LET)的头颈部癌症调强质子治疗稳健优化方法。该方法同时考虑了肿瘤和危及器官(OARs)的LET 和物理剂量分布及其不确定性。

方法和材料

本回顾性研究纳入了 14 例头颈部癌症患者。纳入了脊髓、脑干、脑和口腔。使用两种算法,即体素级最坏情况稳健优化和基于 LET 的稳健优化(LETRO),为每位患者生成调强质子治疗计划。后者方法直接优化 LET 分布,而不是像以前的方法那样间接优化。生成了 LET 体积直方图(LETVHs),并通过 LET 体积约束以用户定义的方式将高 LET 从附近的 OAR 重新分配到肿瘤。计算了剂量-体积直方图指标,如临床靶区(CTV)D 和 D-D、脊髓 D、脑干 D、脑 D 和口腔 D。使用最坏情况分析方法量化计划稳健性。使用类似于剂量-体积直方图指标的 LETVH 指标来描述 LET 分布。使用 Wilcoxon 符号秩检验来衡量统计学意义。

结果

在名义情况下,LETRO 提供了更高的 CTV 中 LET 分布(单位:keV/μm;CTV LET:1.18 比 1.08,LETRO 比 RO,P =.0031),同时保持了可比的物理剂量和计划稳健性。与 RO 相比,LETRO 显著降低了脊髓、脑干和口腔的 LET 分布(脊髓 LET:7.20 比 8.20,P =.0010;脑干 LET:10.95 比 12.05,P =.0007;口腔 LET:2.11 比 3.12,P =.0052),同时在所有 OAR 中具有可比的物理剂量和计划稳健性。在最坏情况下,LETRO 在 CTV 中实现了显著更高的 LET,在脑区中实现了更低的 LET,并且与其他 LETVH 指标相当(CTV LET:3.26 比 3.35,P =.0012;脑 LET:24.80 比 22.00,P =.0016)。

结论

LETRO 稳健地同时优化了 LET 和物理剂量分布。它将高 LET 从 OAR 重新分配到目标,同时略微修改了物理剂量和计划稳健性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验