Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

免疫疗法已成为脑肿瘤治疗的第四大支柱，当与放射疗法相结合时，可能会改善患者的预后并减少神经毒性。与其他联合疗法一样，确定使放射疗法和免疫疗法的协同效应最大化的治疗方案是一项基本挑战。基于机制的数学建模是一种有前途的方法，可以系统地研究治疗组合，以在严格的框架内最大限度地提高积极成果。然而，模型生成的治疗组合的成功临床转化需要患者特定的数据，以允许模型进行有意义的初始化和参数化。定量成像技术已成为一种有前途的高质量来源，用于开发和验证数学模型的空间和时间分辨数据。在这篇综述中，我们将介绍利用患者数据对基于机制的建模框架进行个性化的方法，然后讨论如何利用这些技术通过针对患者的建模和治疗策略优化来改善脑癌预后。