Multiattribute decision theory for IMRT plan selection

IMRT 计划选择的多属性决策理论

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/7563986
• 关键词: Accounting; Address; Algorithms; Characteristics; Clinical; Clinical Trials; Complex; 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; Radiation therapy; Recommendation; Reliance; Research Personnel; Role; Site; Structure; System; Techniques; Time; Uncertainty; Utility Theories; Weight; Work; base; computer based statistical methods; design; improved; preference; programs; tool; treatment planning; tumor; Accounting; Address; Algorithms; Characteristics; Clinical; Clinical Trials; Complex; 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; Radiation therapy; Recommendation; Reliance; Research Personnel; Role; Site; Structure; System; Techniques; Time; Uncertainty; Utility Theories; Weight; Work; base; computer based statistical methods; design; improved; preference; programs; tool; treatment planning; tumor

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.

当前的逆计划方法包含了未教育的决策组件。一些 这些方法的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），以及需要进行多个的需求 繁琐的反复试验试图尝试向计划者提供计划 表征决策过程中固有的权衡。我们建议发展进步 决策过程，将多物原理进化算法与影响图中的影响图 多目标决策环境。假设是，这种方法将导致计划 更紧密地反映了计划者的临床目标，该目标以明确的方式合并了临床的数据 试验，以不确定的方式应用了决策原则，以更临床的方式导致 可接受的计划，并考虑到患者和医师的偏好。我们的方法是 进一步发展我们的反计划功能，以有效地搜索帕累托的可能计划的空间 在案件的多个目标下有效计划。该计划系统将与 影响图。影响图基于贝叶斯网络，该网络合并了专家 医师对计划3D剂量的重要参数的推理和判断 分布和与患者有关的条件。影响图将此图与实用程序节点相结合 这包括医师或患者对可能结果的偏好。我们将发展和 评估前列腺癌和头颈癌的影响图。