Multiattribute decision theory for IMRT plan selection

IMRT 计划选择的多属性决策理论

• 批准号: 7221192
• 负责人: Mark H. Phillips
• 金额: $ 24.17万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-10 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7221192
• 关键词: Accounting; Address; Algorithms; Characteristics; Clinical; Clinical Trials; Complex; Condition; Conformal Radiotherapy; Coupled; Couples; Data; Decision Making; Decision Modeling; Decision Theory; Dose; Effect Modifiers (Epidemiology); Environment; Evaluation; Gambling; Generations; Goals; Head and Neck Cancer; Head and neck structure; Health; Intensity-Modulated Radiotherapy; Judgment; Malignant neoplasm of prostate; Methods; Metric; Outcome; Patient Preferences; Patients; Physicians; Probability; Process; Prostate; Purpose; Radiation therapy; Range; Recommendation; Reliance; Research Personnel; Role; Site; Standards of Weights and Measures; Structure; System; Techniques; Time; Uncertainty; Utility Theories; Weight; Work; base; computer based statistical methods; design; improved; preference; programs; tool; treatment planning; tumor

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：当前的逆计划方法包含了未老练的决策组件。这些方法的某些不良特征包括无法处理不确定性，对用户偏好的原油形式（例如加权因素）的依赖，以及需要进行多种繁琐的，繁琐的反复试图尝试，以便为计划者提供计划在决策过程中固有的计划的计划。我们建议开发一个改进的决策过程，以在多目标决策环境中与影响图的多目标进化算法相结合。假设是，这种方法将导致计划更紧密地反映计划者的临床目标，这些临床目标以明确的方式纳入了临床试验的数据，该数据以不确定性的方式应用了决策原理，以产生更临床上可接受的计划，并考虑了患者和医师的偏好。我们的方法是进一步发展我们的反计划能力，以有效地在案件的多个目标下有效地搜索帕累托有效计划的可能计划的空间。该计划系统将与影响图结合在一起。影响图基于一个贝叶斯网络，该网络结合了专家医师的推理和判断，即计划的3D剂量分布和与患者相关的状况的重要参数。影响图将该图与包括医师或患者有关可能结果的患者偏好的实用程序节点相结合。我们将开发和评估前列腺癌和头颈癌的影响图。