This study considers a navigation method for finding the most preferable radiotherapy plan from a discrete set using planner-defined clinical criteria. The method is based on repeatedly solving an optimization model to identify a plan that best satisfies the aspiration values set by the planner. During navigation, the planner iteratively adjusts the aspiration values to match the preference information learned from previous plans until the most preferable plan is identified. The use of soft constraints to model aspiration values enables navigation amongst a discrete set and allows the planner to freely specify the aspiration values without producing an infeasible model. We demonstrate the use of the model by applying it to a prostate cancer case. This illustrates that improvements in optimization criteria do not necessarily lead to improvements in clinical criteria. Hence, the method obviates the need to simultaneously monitor both optimization and clinical criteria in current navigation systems. Instead, the direct use of clinical criteria for navigation aids the planner to quickly identify the most preferable plan.

中文翻译：

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基于临床标准的外部放射治疗计划的多目标导航

这项研究考虑了一种导航方法，该方法可使用计划者定义的临床标准从离散集合中找到最优选的放射治疗计划。该方法基于反复求解优化模型以识别最能满足计划者设置的期望值的计划。在导航过程中，计划者会反复调整期望值，以匹配从先前计划中获悉的偏好信息，直到确定出最可取的计划为止。使用软约束对吸气值进行建模可以在离散集之间导航，并使计划人员可以自由指定吸气值而不会产生不可行的模型。我们通过将其应用于前列腺癌病例来证明该模型的使用。这说明优化标准的改善并不一定导致临床标准的改善。因此，该方法消除了同时监视当前导航系统中的优化和临床标准的需求。取而代之的是，直接使用临床标准进行导航有助于计划者快速确定最可取的计划。