Multiattribute decision theory for IMRT plan selection

目前的逆向规划方法包含了不复杂的决策部分。其中一些 这些方法的不良特征包括无法处理不确定性、依赖于 用户偏好的粗略形式(例如权重因素)，以及需要通过大量的， 繁琐的反复试错的反向计划尝试，以便向计划者呈现 描述决策过程中固有的权衡。我们建议开发一种改进的 将多目标进化算法与影响图相结合的决策过程 多目标决策环境。假设是，这样的方法将导致计划 更紧密地反映规划者的临床目标，以明确的方式合并来自临床的数据 试验，在不确定的情况下以一种更临床的方式应用决策原则 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计划能力，以有效地搜索帕累托的可能计划空间 一个案例多个目标下的高效计划。这一规划系统将与一个 影响图。影响图基于结合了专家的贝叶斯网络 医生对计划3D剂量的重要参数的推理和判断 分布和与患者相关的情况。影响图将该图与一个实用程序节点结合在一起 这包括医生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。

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.