论文标题
在高模型不确定性的情况下，基于学习的敏感性分析和反馈设计用于癌症混合疗法的药物输送
Learning-Based sensitivity analysis and feedback design for drug delivery of mixed therapy of cancer in the presence of high model uncertainties
论文作者
论文摘要
在本文中，提出了一种方法，该方法能够分析治疗结果的敏感性，以一方面不可避免地会对患者特定参数进行高度分散，并选择定义药物输送反馈策略的参数。更确切地说，给出了一种方法，该方法能够提取和排除最有影响力的参数，以确定给定反馈疗法成功/失败的可能性/失败的可能性，而在不确定性的实现云上，给定的一组初始条件。此外，还可以得出对所使用药物量的期望的预测指标。这使得可以设计一个有效的随机优化框架，以确保肿瘤的安全收缩，同时最大程度地减少所使用的不同药物的加权总和。用涉及三种联合药物的癌症混合疗法的例子来说明和验证该框架：一种化学疗法药物，一种免疫学疫苗和免疫疗法药物。最后，在这种特定情况下，可以表明，仪表板可以在最具影响力状态组件的2D空间中构建，这些状态组件总结了结果的概率和相关的药物用法作为减少状态空间中的ISO值曲线。
In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amounts of drugs being used can also be derived. This enables to design an efficient stochastic optimization framework that guarantees safe contraction of the tumor while minimizing a weighted sum of the quantities of the different drugs being used. The framework is illustrated and validated using the example of a mixed therapy of cancer involving three combined drugs namely: a chemotherapy drug, an immunology vaccine and an immunotherapy drug. Finally, in this specific case, it is shown that dash-boards can be built in the 2D-space of the most influent state components that summarize the outcomes' probabilities and the associated drug usage as iso-values curves in the reduced state space.
