基于强不确定模型的联合癌症治疗概率认证研究
On Probabilistic Certification of Combined Cancer Therapies Using Strongly Uncertain Models
AI总结：
本文提出了一种概率认证框架，用于验证癌症治疗中的肿瘤收缩和循环淋巴细胞下限，以确保在有限时间内高概率成功，克服动态模型不确定性。
摘要：
本文提出了一种通用框架，用于癌症治疗的概率认证。认证基于两个关键问题：肿瘤收缩和循环淋巴细胞的下限界，后者被视为患者健康指标。认证被定义为在有限时间范围内，以预定义的高概率保证治疗成功，尽管动态模型的不可避免的高不确定性会影响最优给药方案的计算。该认证范式可作为调整治疗参数和协议以及合理使用有限或昂贵药物的工具。所提出的框架通过联合免疫疗法/化疗的具体问题进行了说明。
英文摘要：
This paper proposes a general framework for probabilistic certification of cancer therapies. The certification is defined in terms of two key issues which are the tumor contraction and the lower admissible bound on the circulating lymphocytes which is viewed as indicator of the patient health. The certification is viewed as the ability to guarantee with a predefined high probability the success of the therapy over a finite horizon despite of the unavoidable high uncertainties affecting the dynamic model that is used to compute the optimal scheduling of drugs injection. The certification paradigm can be viewed as a tool for tuning the treatment parameters and protocols as well as for getting a rational use of limited or expensive drugs. The proposed framework is illustrated using the specific problem of combined immunotherapy/chemotherapy of cancer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
