Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America.
Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, United States of America.
Biomed Phys Eng Express. 2020 Sep 29;6(6). doi: 10.1088/2057-1976/abb4bc.
To introduce a new optimization algorithm that improves DVH results and is designed for the type of heterogeneous dose distributions that occur in brachytherapy.The new optimization algorithm is based on a prior mathematical approach that uses mean doses of the DVH metric tails. The prior mean dose approach is referred to as conditional value-at-risk (CVaR), and unfortunately produces noticeably worse DVH metric results than gradient-based approaches. We have improved upon the CVaR approach, using the so-called Truncated CVaR (TCVaR), by excluding the hottest or coldest voxels in the structure from the calculations of the mean dose of the tail. Our approach applies an iterative sequence of convex approximations to improve the selection of the excluded voxels. Data Envelopment Analysis was used to quantify the sensitivity of TCVaR results to parameter choice and to compare the quality of a library of 256 TCVaR plans created for each of prostate, breast, and cervix treatment sites with commercially-generated plans.In terms of traditional DVH metrics, TCVaR outperformed CVaR and the improvements increased monotonically as more iterations were used to identify and exclude the hottest/coldest voxels from the optimization problem. TCVaR also outperformed the Eclipse-Brachyvision TPS, with an improvement in PTVD95% (for equivalent organ-at-risk doses) of up to 5% (prostate), 3% (breast), and 1% (cervix).A novel optimization algorithm for HDR treatment planning produced plans with superior DVH metrics compared with a prior convex optimization algorithm as well as Eclipse-Brachyvision. The algorithm is computationally efficient and has potential applications as a primary optimization algorithm or quality assurance for existing optimization approaches.
为介绍一种新的优化算法，该算法改进了剂量体积直方图（DVH）结果，专门针对近距离治疗中出现的不均匀剂量分布类型而设计。新的优化算法基于先前的数学方法，该方法使用 DVH 指标尾部的平均剂量。先前的平均剂量方法称为条件风险价值（CVaR），但不幸的是，它生成的 DVH 指标结果明显比基于梯度的方法差。我们通过从尾部平均剂量的计算中排除结构中最热或最冷的体素来改进 CVaR 方法，称为截断 CVaR（TCVaR）。我们的方法应用一系列迭代凸逼近来改进排除体素的选择。数据包络分析用于量化 TCVaR 结果对参数选择的敏感性，并比较为前列腺、乳腺和宫颈治疗部位创建的 256 个 TCVaR 计划库与商业生成的计划的质量。就传统的 DVH 指标而言，TCVaR 优于 CVaR，并且随着迭代次数的增加，用于从优化问题中识别和排除最热/最冷体素的次数增加，改进效果呈单调递增。TCVaR 也优于 Eclipse-Brachyvision TPS，在等效器官风险剂量（PTVD95%）方面，前列腺、乳腺和宫颈的改善分别高达 5%、3%和 1%。一种新的 HDR 治疗计划优化算法生成的计划与先前的凸优化算法以及 Eclipse-Brachyvision 的计划相比，具有更好的 DVH 指标。该算法计算效率高，具有作为主要优化算法或现有优化方法质量保证的潜在应用。
