Novel optimization methods for cancer screening and treatment

癌症筛查和治疗的新优化方法

• 批准号: RGPIN-2019-05588
• 负责人: Cevik, Mucahit
• 金额: $ 1.89万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738388
• 关键词: Novel; optimization; methods; cancer; screening

Given the recent trends in evidence-based and personalized decision making, powerful data-driven modeling approaches are needed to obtain personalized optimal intervention plans for practical engineering problems. We propose developing novel engineering tools based on stochastic dynamic programming and integer programming to address practical decision making problems. While proposed research will be applied to cancer screening and treatment, the main focus is on developing engineering methodology. We will investigate the applications of large-scale stochastic dynamic programming and integer programming methods in cancer screening and treatment. These application areas are rich with operational problems that potentially provide valuable feedback on limitations of the proposed methodologies, thus opening new avenues in these methodological domains. In particular, sequential decision making problems will be formulated as stochastic dynamic programming models (e.g., partially observable Markov decision process models), and deriving structural properties of the optimal solutions, and exploiting those properties to find efficient solution methods for these models will be explored. Moreover, various decomposition schemes will be proposed for the large-scale integer programming formulations encountered in these application problems. Theme 1 of the program will focus on developing novel methods that can overcome modeling and algorithmic challenges in stochastic dynamic programming. We will investigate personalized cancer screening and treatment problems, and test the viability of the proposed solution methods. Using clinical data from the literature and our collaborators, we will apply these methods to determine optimal policies for cancer screening and treatment. The proposed methodology may help develop insights to improve and personalize cancer screening and treatment practices, yielding a significant contribution as cancer is a leading cause of death in Canada. The proposed approach will also improve the engineering knowledge on stochastic dynamic programming for other engineering applications. Theme 2 of the program will explore large-scale discrete optimization models that arise in radiation therapy treatment planning. Specifically, we will investigate fluence map optimization and decomposition in intensity modulated radiation therapy and sector duration optimization in radiosurgery, which lack efficient solution methods for practical instances. We will develop novel optimization models for these problems and explore the effectiveness of new solution methodologies such as decision diagrams. The results will provide valuable planning, scheduling and operational tools for decision makers. The proposed research will be extended to consider more general settings.

鉴于基于证据和个性化决策的最新趋势，需要强大的数据驱动建模方法来获得针对实际工程问题的个性化最佳干预方案。我们建议开发基于随机动态规划和整数规划的新型工程工具来解决实际决策问题。虽然拟议的研究将应用于癌症筛查和治疗，但主要重点是开发工程方法。我们将研究大规模随机动态规划和整数规划方法在癌症筛查和治疗中的应用。这些应用领域充满了操作问题，这些问题可能对所建议的方法的局限性提供有价值的反馈，从而在这些方法领域开辟了新的途径。特别是，顺序决策问题将被制定为随机动态规划模型（例如，部分可观察马尔可夫决策过程模型），并将探索最优解的结构性质，并利用这些性质找到这些模型的有效解方法。此外，对于这些应用问题中遇到的大规模整数规划公式，将提出各种分解方案。该计划的主题1将侧重于开发能够克服随机动态规划中建模和算法挑战的新方法。我们将研究个性化的癌症筛查和治疗问题，并测试所提出的解决方法的可行性。利用文献中的临床数据和我们的合作者，我们将应用这些方法来确定癌症筛查和治疗的最佳政策。所提议的方法可能有助于形成见解，以改进和个性化癌症筛查和治疗做法，这将产生重大贡献，因为癌症是加拿大的主要死亡原因。所提出的方法也将为其他工程应用提高随机动态规划的工程知识。该计划的主题2将探讨放射治疗计划中出现的大规模离散优化模型。具体而言，我们将研究调强放射治疗中的影响图优化和分解以及放射外科中的扇区持续时间优化，这些问题缺乏实际实例的有效解决方法。我们将为这些问题开发新的优化模型，并探索新的解决方法（如决策图）的有效性。研究结果将为决策者提供有价值的计划、调度和操作工具。拟议的研究将扩大到考虑更一般的情况。