使用遗传算法对单个异心多重大脑转移的PTV边际充足度的评估
José Alejandro Rojas-López1,2, Carlos Daniel Venencia3, Miguel Ángel Chesta1
1Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, X5000HUA, Av. Medina Allende, Córdoba, Argentina.
Biomedical physics & engineering express
|June 13, 2023
概括
在单同位中心多重脑转移放射手术 (SIMM-SRS) 中优化边际通过考虑设置错误来降低健康的大脑剂量. 一种基因算法 (GA) 方法自动调整利率,提高安全性而不会影响目标覆盖率.
科学领域:
- 医学物理 医学物理
- 辐射瘤学 辐射瘤学
- 计算生物学 计算生物学
背景情况:
- 单同中心多重大脑转移的放射性手术 (SIMM-SRS) 是一种复杂的技术,需要精确的边缘定义.
- 基于Linac的放射性外科手术中的设置错误可能会影响剂量覆盖和健康组织暴露.
- 优化利对于平衡治疗有效性和安全性至关重要.
研究的目的:
- 评估优化边际对SIMM-SRS中剂量覆盖率和健康组织剂量的影响.
- 通过使用遗传算法 (GA) 评估设置错误 (旋转/转换) 对SIMM-SRS计划的影响.
- 确定GA驱动的利优化是否可以提高SIMM-SRS的安全性.
主要方法:
- 对32个SIMM-SRS计划 (256个病变) 的质量指数分析,包括帕迪克合格指数 (PCI),梯度指数 (GI) 和剂量指标 (Dmax,Dmean,V12).
- 使用Python包的GA计算6个自由度的诱导设置错误 (0.2°/0.2mm和0.5°/0.5mm) 的最大转移.
- 在不同的错误场景下,将原始计划的质量指数与GA优化的保证金计划进行比较.
主要成果:
- 与原始计划相比,优化利保持了剂量覆盖率 (Dmax,Dmean) (p > 0.072).
- 对于0.5°/0.5mm误差,PCI和GI下降,而健康的大脑V12显著增加.
- 对于0.2°/0.2mm误差,PCI和GI恶化,但健康的大脑V12有所改善.
结论:
- GA促进了自动,个性化的保证金确定,避免了用户的依赖.
- 这种计算方法可以解释SRS的不确定性,使"智能"边际减少以保护健康的大脑组织.
- 临床上可接受的目标体积覆盖率在大多数情况下保持在优化边际.
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