Vaniqui Ana, Canters Richard, Vaassen Femke, Hazelaar Colien, Lubken Indra, Kremer Kirsten, Wolfs Cecile, van Elmpt Wouter
Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands.
Phys Imaging Radiat Oncol. 2020 Oct 19;16:74-80. doi: 10.1016/j.phro.2020.10.006. eCollection 2020 Oct.
Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis.
A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort).
For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and -4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: -0.1 ± 1.1 Gy and -0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning.
An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.
放疗中心常常缺乏用于定期治疗计划验证以及对当前计划质量进行反馈的简单工具。在不同年份或规划过程中衡量治疗质量存在困难。在此，我们通过开发剂量体积直方图（DVH）指标数据库和预测模型来实施计划质量保证（QA）。基于队列分析，这些工具被用于评估直肠癌患者的自动优化治疗计划。
建立了一个治疗计划QA框架，并使用基于重叠体积直方图的模型来预测2018年和2019年接受治疗且根据规划技术分组的患者队列的DVH参数。使用22个重新优化的治疗计划组成的训练队列来构建预测模型。该预测模型在95个自动生成的治疗计划（自动优化队列）和93个手动优化计划（手动优化队列）上进行了验证。
对于手动优化队列，预测的肠袋和膀胱平均剂量偏差分别平均小于0.3±1.4 Gy和 -4.3±5.5 Gy；对于自动优化队列，观察到的偏差更小，分别为 -0.1±1.1 Gy和 -0.2±2.5 Gy。自动优化队列的DVH参数四分位距平均更小，表明与手动规划相比，每个参数内的变化更小。
一个使用DVH预测模型监测治疗质量的自动化框架已成功在临床上实施，并且显示与手动优化计划相比，自动计划的DVH参数变化更小。该框架还允许进行个体反馈和DVH估计。
