放射治疗（RT）是最常见的抗癌疗法之一。然而，尽管患者之间的放射敏感性和伴随的治疗反应存在很大差异，但当前的放射肿瘤学实践并未针对个体患者调整放疗剂量。我们之前已经证明，肿瘤体积动态的机械数学模型可以模拟个体患者对放疗的体积反应，并估计个性化放疗剂量，以实现最佳的肿瘤体积缩小。然而，在计算个性化放疗剂量时，了解基础放疗反应模型选择的影响至关重要。在本研究中，我们评估了两种放疗反应模型对剂量个性化的数学意义和生物学效应：(1) 对癌症的细胞毒性导致直接肿瘤体积缩小（DVR）的细胞和（2）对肿瘤微环境的辐射反应，导致肿瘤承载能力降低（CCR）和随后的肿瘤缩小。肿瘤生长被模拟为逻辑生长，治疗前动态在增殖饱和指数（PSI）中描述。根据每个相应的模型模拟 RT 的效果，以制定标准的分次放疗计划，工作日分次剂量为 2 Gy。对两个模型的固有肿瘤生长速率和放射敏感性参数进行参数扫描评估，以观察每个模型参数的定性影响。然后，我们计算了全范围放射敏感性和增殖饱和值的所有组合中局部肿瘤控制 (LRC) 所需的最小 RT 剂量。两种模型均估计放射敏感性较高的患者需要较低的 RT 剂量才能实现 LRC。然而，这两个模型对 PSI 对 LRC 最小 RT 剂量的影响做出了相反的估计：DVR 模型估计 PSI 值较高的肿瘤将需要更高的 RT 剂量才能实现 LRC，而 CCR 模型估计 PSI 值较高的肿瘤将需要更高的 RT 剂量来实现 LRC。需要较低的 RT 剂量才能实现 LRC。最终，这些结果表明，在使用任何此类模型来估计个性化治疗建议之前，了解哪种模型最能描述特定环境下的肿瘤生长和治疗反应的重要性。版权所有 © 2023 Kutuva 、考德尔、亚莫阿、恩德林和扎希德。

Radiation therapy (RT) is one of the most common anticancer therapies. Yet, current radiation oncology practice does not adapt RT dose for individual patients, despite wide interpatient variability in radiosensitivity and accompanying treatment response. We have previously shown that mechanistic mathematical modeling of tumor volume dynamics can simulate volumetric response to RT for individual patients and estimation personalized RT dose for optimal tumor volume reduction. However, understanding the implications of the choice of the underlying RT response model is critical when calculating personalized RT dose.In this study, we evaluate the mathematical implications and biological effects of 2 models of RT response on dose personalization: (1) cytotoxicity to cancer cells that lead to direct tumor volume reduction (DVR) and (2) radiation responses to the tumor microenvironment that lead to tumor carrying capacity reduction (CCR) and subsequent tumor shrinkage. Tumor growth was simulated as logistic growth with pre-treatment dynamics being described in the proliferation saturation index (PSI). The effect of RT was simulated according to each respective model for a standard schedule of fractionated RT with 2 Gy weekday fractions. Parameter sweeps were evaluated for the intrinsic tumor growth rate and the radiosensitivity parameter for both models to observe the qualitative impact of each model parameter. We then calculated the minimum RT dose required for locoregional tumor control (LRC) across all combinations of the full range of radiosensitvity and proliferation saturation values.Both models estimate that patients with higher radiosensitivity will require a lower RT dose to achieve LRC. However, the two models make opposite estimates on the impact of PSI on the minimum RT dose for LRC: the DVR model estimates that tumors with higher PSI values will require a higher RT dose to achieve LRC, while the CCR model estimates that higher PSI values will require a lower RT dose to achieve LRC.Ultimately, these results show the importance of understanding which model best describes tumor growth and treatment response in a particular setting, before using any such model to make estimates for personalized treatment recommendations.Copyright © 2023 Kutuva, Caudell, Yamoah, Enderling and Zahid.