IEEE Trans Biomed Eng. 2021 Oct;68(10):2907-2917. doi: 10.1109/TBME.2021.3055822. Epub 2021 Sep 20.
Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front.
Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM).
On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk.
Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners.
Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.
放射治疗计划是一个耗时的、迭代的过程，具有潜在的高计划者间变异性。完全自动化的治疗计划过程可以减少规划师的主动治疗计划时间，并消除计划者间的变异性，有可能极大地提高患者周转率和护理质量。在开发治疗计划的完全自动化算法时，我们有两个主要目标：生成 1）帕累托最优和 2）临床可接受的计划。在这里，我们提出了帕累托最优投影搜索（POPS）算法，该算法为直接搜索帕累托前沿提供了一个通用框架。
我们的 POPS 算法是一种新颖的自动化规划方法，它结合了两种主要的搜索过程：1）决策变量空间中的无梯度搜索，2）使用二分法将决策变量投影到帕累托前沿。我们通过与临床治疗计划进行比较来展示 POPS 的性能。作为治疗计划质量的一种可能的定量衡量标准，我们构建了一个临床可接受性评分函数（SF），该函数是从先前开发的通用评估指标（GEM）修改而来的。
在作为临床工作流程一部分收集的 21 例前列腺病例数据集上，我们提出的 POPS 算法生成了帕累托最优的计划，在剂量一致性、剂量均匀性和保护危及器官方面具有临床可接受性。
我们提出的 POPS 算法为完全自动化的治疗计划提供了一个通用框架，在不要求人类规划师主动参与的情况下，实现了临床可接受的剂量质量。
我们的完全自动化 POPS 算法解决了其他自动化规划方法的许多关键限制，我们预计它将极大地改善治疗计划工作流程。
