Multiattribute decision theory for IMRT plan selection

描述（由申请人提供）：当前的逆向规划方法包含了简单的决策组成部分。这些方法的一些不良特征包括无法处理不确定性，依赖于用户偏好的粗糙形式（如加权因子），以及需要进行大量繁琐的试错逆规划尝试，以便向规划人员提供具有决策过程中固有权衡特征的计划。我们建议在多目标决策环境中开发一种改进的决策过程，将多目标进化算法与影响图耦合在一起。我们的假设是，这样的方法将使计划更紧密地反映出计划者的临床目标，以一种明确的方式纳入临床试验的数据，在不确定的情况下应用决策原则，从而产生更多临床可接受的计划，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向规划能力，在一个案例的多目标下，有效地搜索Pareto有效方案的可能方案空间。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对计划的3D剂量分布和患者相关条件的重要参数的推理和判断。影响图将此图与效用节点相结合，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.