Department of Radiation Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, USA.
Med Phys. 2021 May;48(5):2108-2117. doi: 10.1002/mp.14775. Epub 2021 Mar 20.
Permanent low-dose-rate brachytherapy is a widely used treatment modality for managing prostate cancer. In such interventions, treatment planning can be a challenging task and requires experience and skills of the planner. We developed a novel knowledge-based (KB) optimization method based on previous treatment plans. The purpose of this method was to generate clinically acceptable plans that do not require extensive manual adjustments in clinical scenarios.
Objective functions used in current inverse planning methods are preferably based on spatial invariant dose objectives rather than spatial dose distributions. Therefore, they are prone to return suboptimal plans resulting in time consuming plan adjustments. To overcome this limitation, a KB approach is introduced. The KB model uses the dose distributions of previous clinical plans projected onto a standardized geometry. From those standardized distributions a template plan is generated. The treatment plans were optimized with an in-house developed planning system by solving a constraint inverse optimization problem that mimics the projected template dose plan constraint to DVH metrics. The method is benchmarked under an IRB-approved retrospective study by comparing optimization time, dosimetric performance, and clinical acceptability against current clinical practice. The quality of the KB model is evaluated with a Turing test.
The KB model consists of five high-quality treatment plans. Those plans were selected by one of our experts and showed all desired dosimetric features. After generating the model treatment plans were created with one run of the optimizer for the remaining 20 patients. The optimization time including needle optimization ranged from 6 to 29 s. Based on a Wilcoxon signed rank test the new plans are dosimetrically equivalent to current clinical practice. The Turing test showed that the proposed method generates plans that are equivalent to current clinical practice and that the dose prediction drives the optimization to achieve high-quality treatment plans.
This study demonstrated that the proposed KB model was able to capture user-specific features in isodose lines which can be used to generate acceptable treatment plans with a single run of the optimization engine in under a minute. This could potentially reduce the time in the operating room and the time a patient is under anesthesia.
永久低剂量率近距离放射治疗是治疗前列腺癌的一种广泛应用的治疗方法。在这种介入治疗中，治疗计划可能是一项具有挑战性的任务，需要规划者的经验和技能。我们开发了一种基于先前治疗计划的新型基于知识的 (KB) 优化方法。该方法的目的是生成在临床情况下不需要大量手动调整的临床可接受的计划。
当前逆规划方法中使用的目标函数最好基于空间不变剂量目标，而不是空间剂量分布。因此，它们容易返回次优的计划，导致耗时的计划调整。为了克服这个限制，引入了一种 KB 方法。KB 模型使用先前临床计划的剂量分布投影到标准化几何形状上。从那些标准化的分布中生成一个模板计划。通过求解约束逆优化问题来优化治疗计划，该问题模拟了投影模板剂量计划约束到剂量体积直方图 (DVH) 指标。该方法通过与当前临床实践进行比较，在经过机构审查委员会批准的回顾性研究中进行了基准测试，比较了优化时间、剂量学性能和临床可接受性。使用图灵测试评估 KB 模型的质量。
KB 模型由五个高质量的治疗计划组成。这些计划由我们的一位专家选择，展示了所有所需的剂量学特征。生成模型后，仅需一次运行优化器即可为其余 20 名患者创建治疗计划。包括针优化在内的优化时间范围为 6 至 29 秒。基于 Wilcoxon 符号秩检验，新计划在剂量学上与当前临床实践等效。图灵测试表明，所提出的方法生成的计划与当前临床实践等效，并且剂量预测驱动优化以实现高质量的治疗计划。
这项研究表明，所提出的 KB 模型能够捕获等剂量线中的用户特定特征，可用于在不到一分钟的时间内使用优化引擎的单次运行生成可接受的治疗计划。这可能会减少手术室中的时间和患者麻醉下的时间。
