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用于确定晶格放射治疗中顶点的优化方法。

Optimization method for determining vertices in lattice radiotherapy.

作者信息

Ma Pan, Xu Yingjie, Yao Yuhe, Lu Ningning, Dai Jianrong

机构信息

Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

出版信息

Front Oncol. 2025 Apr 30;15:1582402. doi: 10.3389/fonc.2025.1582402. eCollection 2025.

Abstract

PURPOSE

This study presents an optimization method for arranging lattice radiotherapy (LRT) targets to enhance the contrast between peak and valley doses, aiming to improve the treatment effectiveness and precision.

MATERIALS AND METHODS

The LRT target comprises multiple sphere-like vertices generated using the optimization method, which involves four steps: 1) generating a volume for vertex arrangement, 2) determining initial positions and size of packing units, 3) determining initial positions and size of all the vertices and 4) optimizing the final vertex positions by using adaptive simulated annealing (ASA). Volumetric modulated arc therapy plans were retrospectively regenerated using the initial vertices produced by closest packing (Plan_Clo) and vertices obtained after ASA optimization (Plan_Opt). The peak-to-valley index (PVI) that characterizes the difference between peak and valley doses was introduced to evaluate the performance.

RESULTS

A statistically significant difference was observed in the average PVI between Plan_Clo and Plan_Opt (p = 0.000). The average PVI ratio for Plan_Opt compared to Plan_Clo was 5.95 ± 4.87 (range: 1.24-16.80).

CONCLUSION

The proposed optimization method for determining LRT target vertices has been validated, demonstrating a significant improvement in the PVI. ASA optimization, combined with closest packing, effectively enhanced the peak-to-valley dose difference in LRT, showcasing its potential for advancing treatment planning.

摘要

目的

本研究提出一种优化方法,用于排列点阵放射治疗(LRT)靶点,以增强峰剂量与谷剂量之间的对比度,旨在提高治疗效果和精度。

材料与方法

LRT靶点由使用该优化方法生成的多个球状顶点组成,该方法包括四个步骤:1)生成用于顶点排列的体积;2)确定填充单元的初始位置和大小;3)确定所有顶点的初始位置和大小;4)使用自适应模拟退火(ASA)优化最终顶点位置。使用由紧密堆积产生的初始顶点(Plan_Clo)和ASA优化后获得的顶点,回顾性地重新生成容积调强弧形治疗计划。引入表征峰剂量与谷剂量差异的峰谷指数(PVI)来评估性能。

结果

在Plan_Clo和Plan_Opt之间的平均PVI中观察到统计学上的显著差异(p = 0.000)。与Plan_Clo相比,Plan_Opt的平均PVI比值为5.95±4.87(范围:1.24 - 16.80)。

结论

所提出的用于确定LRT靶点顶点的优化方法已得到验证,显示出PVI有显著改善。ASA优化与紧密堆积相结合,有效地增强了LRT中的峰谷剂量差异,展示了其在推进治疗计划方面的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b9b/12075192/d94cf1575f2a/fonc-15-1582402-g001.jpg

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