免疫疗法是一个快速发展的领域，许多模型试图描述其对免疫系统的影响，特别是与放射疗法结合使用时。肿瘤对这种组合的反应涉及复杂的时空动态，这使得在由此产生的广泛解决方案空间中进行临床或临床前体内研究变得极其困难。在这篇综述中，研究了放射治疗、免疫治疗和患者免疫系统之间相互作用的几种计算机模型。该研究仅包括以英文发表的数学模型，研究放疗对免疫系统的影响，或免疫检查点抑制剂免疫放疗的影响。研究结果表明，与单独放疗或免疫治疗相比，同时进行放疗和免疫治疗时，预计治疗效果会有所改善。然而，这些模型在放射治疗和免疫治疗的最佳时间表和分割方面并不一致。这与相关的临床试验相对应，这些临床试验报告了联合治疗的治疗效果有所改善，但是，不同临床试验的最佳时间安排有所不同。模型之间的这种差异可归因于模型方法和建模的特定癌症类型的变化，使得确定最佳的一般治疗方案和模型具有挑战性。需要使用类似的数据集进行进一步的研究，以评估特定癌症类型和阶段的最佳模型和治疗方案。© 2024。Crown。

Immunotherapy is a rapidly evolving field, with many models attempting to describe its impact on the immune system, especially when paired with radiotherapy. Tumor response to this combination involves a complex spatiotemporal dynamic which makes either clinical or pre-clinical in vivo investigation across the resulting extensive solution space extremely difficult. In this review, several in silico models of the interaction between radiotherapy, immunotherapy, and the patient's immune system are examined. The study included only mathematical models published in English that investigated the effects of radiotherapy on the immune system, or the effect of immuno-radiotherapy with immune checkpoint inhibitors. The findings indicate that treatment efficacy was predicted to improve when both radiotherapy and immunotherapy were administered, compared to radiotherapy or immunotherapy alone. However, the models do not agree on the optimal schedule and fractionation of radiotherapy and immunotherapy. This corresponds to relevant clinical trials, which report an improved treatment efficacy with combination therapy, however, the optimal scheduling varies between clinical trials. This discrepancy between the models can be attributed to the variation in model approach and the specific cancer types modeled, making the determination of the optimum general treatment schedule and model challenging. Further research needs to be conducted with similar data sets to evaluate the best model and treatment schedule for a specific cancer type and stage.© 2024. Crown.