An in-silico study of conventional and FLASH radiotherapy iso-effectiveness: potential impact of radiolytic oxygen depletion on tumor growth curves and tumor control probability.

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL
I González-Crespo, F Gómez, Ó López Pouso, J Pardo-Montero
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引用次数: 0

Abstract

Objective. This work aims to investigate the iso-effectiveness of conventional and FLASH radiotherapy on tumors through in-silico mathematical models. We focused on the role of radiolytic oxygen depletion (ROD), which has been argued as a possible factor to explain the FLASH effect.Approach. We used a spatiotemporal reaction-diffusion model, including ROD, to simulate tumor oxygenation and response. From those oxygen distributions we obtained surviving fractions (SFs) using the linear-quadratic (LQ) model with the oxygen enhancement ratios (OERs). We then employed the calculated SFs to describe the evolution of preclinical tumor volumes through a mathematical model of tumor response, and we also extrapolated those results to calculate tumor control probabilities (TCPs) using the Poisson-LQ approach.Main results. Our study suggests that the ROD effect may cause differences in SF between FLASH and conventional radiotherapy, especially in lowα/βandpoorly oxygenatedcells. However, a statistical analysis showed that these changes in SF generally do not result in significant differences in the evolution of preclinical tumor growth curves when the sample size is small, because such differences in SF may not be noticeable in the heterogeneity of the population of animals. Nonetheless, when extrapolating this effect to TCP curves, we observed important differences between both techniques (TCP is lower in FLASH radiotherapy). When analyzing the response of tumors with heterogeneous oxygenations, differences in TCP are more important forwell oxygenatedtumors. This apparent contradiction with the results obtained for homogeneously oxygenated cells is explained by the complex interplay between the heterogeneity of tumor oxygenation, the OER effect, and the ROD effect.Significance. This study supports the experimentally observed iso-effectiveness of FLASH and conventional radiotherapy when analyzing the volume evolution of preclinical tumors (that are far from control). However, this study also hints that tumor growth curves may be less sensitive to small variations in SF than tumor control probability: ROD may lead to increased SF in FLASH radiotherapy, which while not large enough to cause significant differences in tumor growth curves, could lead to important differences in clinical TCPs. Nonetheless, it cannot be discarded that other effects not modeled in this work, like radiation-induced immune effects, can contribute to tumor control and maintain the iso-effectiveness of FLASH radiotherapy. The study of tumor growth curves may not be the ideal experiment to test the iso-effectiveness of FLASH, and experiments reporting TCP orD50may be preferred.

传统放疗和FLASH放疗等效性模拟研究:放射性氧耗竭对肿瘤生长曲线和肿瘤控制概率的潜在影响。
目的:这项工作旨在通过室内数学模型研究传统放疗和FLASH放疗对肿瘤的等效性。我们重点研究了放射性氧耗竭(ROD)的作用,ROD 被认为是解释 FLASH 效果的一个可能因素:我们使用时空反应-扩散模型(包括 ROD)来模拟肿瘤氧合和反应。根据这些氧分布,我们利用线性二次方(LQ)模型和氧增强比(OER)获得了存活分数(SFs)。然后,我们利用计算出的 SFs,通过肿瘤反应数学模型来描述临床前肿瘤体积的演变,并利用泊松-LQ 方法推断这些结果,计算肿瘤控制概率 (TCP):我们的研究表明,ROD效应可能会导致FLASH与传统放疗之间的SF差异,尤其是在α/β值低和氧合作用差的细胞中。然而,统计分析显示,当样本量较小时,SF 的这些变化一般不会导致临床前肿瘤生长曲线演变的显著差异,因为在动物群体的异质性中,SF 的这种差异可能并不明显}。然而,当把这种效应推断到 TCP 曲线时,我们观察到两种技术之间存在着重要差异(FLASH 放射疗法的 TCP 较低)。在分析氧合不均匀肿瘤的反应时,氧合良好肿瘤的 TCP 差异更为重要。这与均匀氧合细胞的结果明显矛盾,原因在于肿瘤氧合的异质性、OER效应和ROD效应之间复杂的相互作用:在分析临床前肿瘤(远离对照)的体积演变时,本研究支持实验观察到的 FLASH 和传统放疗的等效性。不过,这项研究也暗示,与肿瘤控制概率相比,肿瘤生长曲线可能对 SF 的微小变化不那么敏感:ROD可能会导致FLASH放疗中SF的增加,虽然不足以导致肿瘤生长曲线的显著差异,但可能会导致临床TCP的重要差异。尽管如此,也不能排除本研究未模拟的其他效应,如辐射诱导的免疫效应,也会促进肿瘤控制并保持 FLASH 放射治疗的等效性。肿瘤生长曲线研究可能不是测试 FLASH 等效性的理想实验,报告 TCP 或 D50 的实验可能更可取。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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