Département de physique, de génie physique et d'optique et Centre de recherche sur le cancer de l'Université Laval, CHU de Québec, Québec, Canada; Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada.
Département de radio-oncologie et Centre de recherche du CHU de Québec, CHU de Québec - Université Laval, Québec, Canada.
Brachytherapy. 2020 Sep-Oct;19(5):607-617. doi: 10.1016/j.brachy.2020.06.016. Epub 2020 Jul 23.
Currently in high-dose-rate (HDR) brachytherapy planning, manual fine-tuning of an objective function is a common practice. Furthermore, automated planning approaches such as multicriteria optimization (MCO) are still limited to the automatic generation of a single treatment plan. This study aims to quantify planning efficiency gains when using a graphics processing unit-based MCO (gMCO) algorithm combined with a novel graphical user interface (gMCO-GUI) that integrates efficient automated and interactive plan navigation tools.
The gMCO algorithm was used to generate 1000 Pareto optimal plans per case for 379 prostate cases. gMCO-GUI was developed to allow plan navigation through all plans. gMCO-GUI integrates interactive parameter selection tools directly with the optimization algorithm to allow plan navigation. The quality of each plan was evaluated based on the Radiation Treatment Oncology Group 0924 protocol and a more stringent institutional protocol (INSTp). gMCO-GUI allows real-time time display of the dose-volume histogram indices, the dose-volume histogram curves, and the isodose lines during the plan navigation.
Over the 379 cases, the fraction of Radiation Treatment Oncology Group 0924 protocol valid plans with target coverage greater than 95% was 90.8%, compared with 66.0% for clinical plans. The fraction of INSTp valid plans with target coverage greater than 95% was 81.8%, compared with 62.3% for clinical plans. The average time to compute 1000 deliverable plans with gMCO was 12.5 s, including the full computation of the 3D dose distributions.
Combining the gMCO algorithm with automated and interactive plan navigation tools resulted in simultaneous gains in both plan quality and planning efficiency.
目前在高剂量率（HDR）近距离放射治疗计划中，手动调整目标函数是一种常见做法。此外，自动化计划方法，如多标准优化（MCO），仍然仅限于自动生成单个治疗计划。本研究旨在量化使用基于图形处理单元的 MCO（gMCO）算法结合新的图形用户界面（gMCO-GUI）的计划效率增益，该界面集成了高效的自动化和交互式计划导航工具。
使用 gMCO 算法为 379 例前列腺病例中的每例生成 1000 个 Pareto 最优计划。gMCO-GUI 用于允许通过所有计划进行计划导航。gMCO-GUI 通过直接与优化算法集成交互式参数选择工具来允许计划导航。根据放射治疗肿瘤学组 0924 协议和更严格的机构协议（INSTp）评估每个计划的质量。gMCO-GUI 允许在计划导航期间实时显示剂量-体积直方图指标、剂量-体积直方图曲线和等剂量线。
在 379 例病例中，根据放射治疗肿瘤学组 0924 协议，目标覆盖率大于 95%的有效计划比例为 90.8%，而临床计划为 66.0%。根据 INSTp，目标覆盖率大于 95%的有效计划比例为 81.8%，而临床计划为 62.3%。使用 gMCO 计算 1000 个可交付计划的平均时间为 12.5 秒，包括 3D 剂量分布的完整计算。
将 gMCO 算法与自动化和交互式计划导航工具相结合，同时提高了计划质量和规划效率。
