Robust Multi-Criteria Optimization With Application to Radiation Therapy

稳健的多标准优化及其在放射治疗中的应用

• 批准号: 1463489
• 负责人: Omid Nohadani
• 金额: $ 30.93万
• 依托单位: Northwestern University at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1463489&HistoricalAwards=false
• 关键词: Robust; Multi; Criteria; Optimization; Application

The research goal of this award is to significantly expand the range of applicability of multi-criteria optimization when input data are uncertain and may exhibit correlations. This includes the collection and analysis of potentially erroneous data, the research of robustness in correlated multi-expert multi-criteria problems, and the validation of the methods on available approaches and probing of results on real data. Many real-world applications put a decision maker in the position to simultaneously achieve multiple contradicting goals. The most common approach is to minimize an aggregate of the objectives, each of which are given a positive weight by its importance. Often, these weights are determined by one or more experts, leading to a sizable level of uncertainty. The results from this research will advance the incorporation of experts' preferences into decision- making and the reliability and reproducibility of decisions. The generality of the approach makes it broadly relevant to real-world problems, many with objectives that are naturally correlated. In radiation therapy, the approach enables oncologists' choices to directly inform the method. Human bias in clinical decisions will be minimized, making optimal cancer treatment accessible, relevant, and ultimately beneficial to patients regardless of medical facility or geographic region. From an educational standpoint, the direct interaction between engineering students and clinicians will offer a unique training ground for the next generation of cross-disciplinary researchers.Currently, the two main paths in multi-criteria optimization in the presence of errors are Pareto frontier approaches that do not produce one final decision and distributionally robust approaches that are limited to the availability of the distribution. This project focuses on an extension of the latter and is motivated by the application in radiation therapy optimization, as used in nearly two thirds of all cancer cases. This inverse problem typically follows a trial-and-error procedure. The research agenda provides deeper insights on a number of levels into fundamental questions in robust multi-criteria decision-making problems, as well as to establish tools and solutions directly relevant to clinical practice. This project, if successful, can lead to a drastic reduction of both decision time and ambiguity of the outcome, warranting high-quality cancer radiation treatments not limited by human and institutional uncertainties.

该奖项的研究目标是在输入数据不确定且可能存在相关性的情况下，显着扩大多准则优化的适用范围。 这包括潜在错误数据的收集和分析，相关的多专家多准则问题的鲁棒性研究，以及对现有方法的验证和对真实的数据的结果的探测。 许多现实世界的应用程序将决策者置于同时实现多个相互矛盾的目标的位置。 最常见的方法是最小化目标的总和，每个目标都根据其重要性给予正权重。 通常，这些权重由一个或多个专家确定，导致相当大的不确定性。 这项研究的结果将促进专家偏好纳入决策以及决策的可靠性和可重复性。该方法的通用性使其广泛适用于现实世界的问题，许多目标是自然相关的。在放射治疗中，这种方法使肿瘤学家的选择能够直接告知方法。临床决策中的人为偏见将被最小化，使最佳癌症治疗变得可获得，相关，并最终对患者有益，无论医疗设施或地理区域如何。 从教育的角度来看，工程专业的学生和临床医生之间的直接互动将提供一个独特的培训基地，为下一代的跨学科researchers.Currently，在多标准优化中存在的错误的两个主要途径是帕累托前沿的方法，不产生一个最终的决定和分布鲁棒的方法，是有限的分布的可用性。该项目的重点是后者的扩展，其动机是在放射治疗优化中的应用，因为近三分之二的癌症病例都使用了这种方法。这个逆问题通常遵循试错过程。研究议程提供了更深入的见解，在一些层面上的基本问题，在强大的多标准决策问题，以及建立直接相关的工具和解决方案的临床实践。 该项目如果成功，可以大大减少决策时间和结果的模糊性，从而实现不受人为和机构不确定性限制的高质量癌症放射治疗。