Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Phys Med Biol. 2020 Apr 2;65(7):075009. doi: 10.1088/1361-6560/ab7362.
We present an automatic bi-objective parameter-tuning approach for inverse planning methods for high-dose-rate prostate brachytherapy, which aims to overcome the difficult and time-consuming manual parameter tuning that is currently required to obtain patient-specific high-quality treatment plans. We modelled treatment planning as a bi-objective optimization problem, in which dose-volume-based planning criteria related to target coverage are explicitly separated from organ-sparing criteria. When this model is optimized, a large set of high-quality plans with different trade-offs can be obtained. This set can be visualized as an insightful patient-specific trade-off curve. In our parameter-tuning approach, the parameters of inverse planning methods are automatically tuned, aimed to maximize the two objectives of the bi-objective planning model. By generating trade-off curves for different inverse planning methods, their maximally achievable plan quality can be insightfully compared. Automatic parameter tuning furthermore allows to construct standard parameter sets (class solutions) representing different trade-offs in a principled way, which can be directly used in current clinical practice. In this work, we considered the inverse planning methods IPSA and HIPO. Thirty-nine previously treated prostate cancer patients were included. We compared automatic parameter tuning, random parameter sampling, and the maximally achievable plan quality obtained by directly optimizing the bi-objective planning model with the state-of-the-art optimization software GOMEA. We showed that for each patient, a set of plans with a wide range of trade-offs could be obtained using automatic parameter tuning for both IPSA and HIPO. By tuning HIPO, better trade-offs were obtained than by tuning IPSA. For most patients, automatic tuning of HIPO resulted in plans close to the maximally achievable plan quality obtained by optimizing the bi-objective planning model directly. Automatic parameter tuning was shown to improve plan quality significantly compared to random parameter sampling. Finally, from the automatically-tuned plans, three class solutions were successfully constructed representing different trade-offs.
我们提出了一种用于高剂量率前列腺近距离治疗逆向计划方法的自动双目标参数调整方法，旨在克服当前获得患者特异性高质量治疗计划所需的困难和耗时的手动参数调整。我们将治疗计划建模为一个双目标优化问题，其中与目标覆盖相关的剂量-体积规划标准与器官保护标准明确分开。当优化这个模型时，可以得到大量具有不同权衡的高质量计划。这个集合可以被可视化成一个有洞察力的患者特异性权衡曲线。在我们的参数调整方法中，自动调整逆向计划方法的参数，旨在最大化双目标规划模型的两个目标。通过生成不同逆向计划方法的权衡曲线，可以直观地比较它们最大可实现的计划质量。自动参数调整还允许以有原则的方式构建代表不同权衡的标准参数集（类解决方案），这些参数集可以直接用于当前的临床实践。在这项工作中，我们考虑了 IPSA 和 HIPO 这两种逆向计划方法。纳入了 39 名先前接受过前列腺癌治疗的患者。我们比较了自动参数调整、随机参数抽样以及使用最先进的优化软件 GOMEA 直接优化双目标规划模型所能获得的最大可实现计划质量。我们表明，对于每个患者，都可以使用 IPSA 和 HIPO 的自动参数调整获得具有广泛权衡的一系列计划。通过调整 HIPO，可以获得比调整 IPSA 更好的权衡。对于大多数患者，HIPO 的自动调整导致的计划接近直接优化双目标规划模型所能获得的最大可实现计划质量。与随机参数抽样相比，自动参数调整显著提高了计划质量。最后，从自动调整的计划中，成功构建了三个类解决方案，代表了不同的权衡。
