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）：目前的逆向规划方法包含简单的决策组件。这些方法的一些不可取的特点，包括无法处理的不确定性，依赖于粗糙的形式（如加权因子）的用户偏好，并需要通过大量的，繁琐的试错逆向规划尝试，以目前的规划师与计划，其特点是权衡决策过程中固有的。我们建议开发一个改进的决策过程，耦合多目标进化算法与影响图在多目标决策环境。假设是，这样的方法将导致计划，更密切地反映规划者的临床目标，以明确的方式纳入临床试验的数据，在不确定性的情况下应用决策的原则，从而导致更多的临床可接受的计划，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规划能力，有效地搜索空间的可能计划的帕累托有效计划下的多个目标的情况下。该规划系统将与影响图相结合。影响图基于贝叶斯网络，该网络结合了专家医生关于计划的3D剂量分布和患者相关条件的重要参数的推理和判断。影响图将该图与效用节点相结合，该效用节点包括关于可能结果的医生或患者偏好。我们将开发和评估前列腺癌和头颈癌的影响图。