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Multiattribute decision theory for IMRT plan selection

IMRT 计划选择的多属性决策理论

基本信息

批准号：
7563986
负责人：
Mark H. Phillips
金额：
$ 24.2万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-04-10 至 2011-02-28
项目状态：
已结题

项目摘要

Current methods of inverse planning incorporate unsophisticated decision-making components. Some of the undesirable characteristics of these methods include the inability to deal with uncertainty, the reliance on crude forms (such as weighting factors) of user preferences, and the need to proceed through numerous, cumbersome trial-and-error inverse planning attempts in order to present the planner with plans that characterize the trade-offs inherent in the decision making process. We propose to develop an improved decision making process that couples multiobjective evolutionary algorithms with influence diagrams in a multiobjective decision making environment. The hypothesis is that such an approach will result in plans that more closely reflect the planner's clinical goals, that incorporate in an explicit manner the data from clinical trials, that apply the principles of decision making under uncertainty in a way that results in more clinically acceptable plans, and that take into account the preferences of the patient and physician. Our approach is to further develop our inverse planning capabilities to efficiently search the space of possible plans for Pareto efficient plans under the multiple objectives of a case. This planning system will be coupled with an influence diagram. The influence diagram is based on a Bayesian network that incorporates expert physicians' reasoning and judgements regarding the important parameters of both the plan's 3D dose distribution and patient-related conditions. The influence diagram combines this diagram with a utility node that includes physician or patient preferences regarding the possible outcomes. We will develop and evaluate influence diagrams for prostate cancer and for head & neck cancer.

当前的逆向规划方法包含不复杂的决策成分。一些这些方法的不良特征包括无法处理不确定性、依赖用户偏好的粗略形式（例如权重因子），以及需要进行大量、繁琐的试错逆向规划尝试，以便向规划者提供以下计划：描述决策过程中固有的权衡。我们建议开发一种改进的将多目标进化算法与影响图结合起来的决策过程多目标决策环境。假设这种方法将产生以下计划：更密切地反映规划者的临床目标，以明确的方式整合来自临床的数据试验，以一种更加临床化的方式应用不确定性下的决策原则可接受的计划，并考虑到患者和医生的偏好。我们的方法是进一步发展我们的逆向规划能力，以有效地搜索帕累托可能计划的空间一个案例的多个目标下的高效计划。该规划系统将与影响图。影响图基于包含专家的贝叶斯网络医生对计划 3D 剂量的重要参数的推理和判断分布和患者相关情况。影响图将该图与实用节点结合起来其中包括医生或患者对可能结果的偏好。我们将开发和评估前列腺癌和头颈癌的影响图。