Multiattribute decision theory for IMRT plan selection

描述(由申请人提供)：目前的逆向规划方法包含了不复杂的决策部分。这些方法的一些不受欢迎的特点包括：不能处理不确定性，依赖于用户偏好的原始形式(如加权系数)，以及需要进行大量繁琐的反复试错的反向计划尝试，以便向规划者呈现以决策过程中固有的权衡为特征的计划。我们建议开发一种改进的决策过程，将多目标进化算法与多目标决策环境中的影响图相结合。假设这样的方法将产生更紧密地反映计划者的临床目标的计划，以明确的方式合并来自临床试验的数据，以一种导致更多临床可接受的计划的方式应用不确定情况下的决策原则，并考虑患者和医生的偏好。我们的方法是进一步发展我们的逆向计划能力，以便在一个案例的多个目标下有效地搜索可能的计划空间，以获得帕累托有效计划。这个计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对计划的3D剂量分布和与患者相关的情况的重要参数的推理和判断。影响图将该图与一个效用节点结合在一起，该效用节点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。

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.