Yoganathan S A, Basith Ahamed, Rostami Aram, Usman Muhammad, Paloor Satheesh, Hammoud Rabih, Al-Hammadi Noora
Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Department of Radiation Oncology, National Center for Cancer Care & Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
Med Dosim. 2025;50(1):8-12. doi: 10.1016/j.meddos.2024.06.003. Epub 2024 Jul 29.
Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP). Seventy-four patients with head-and-neck (HN) cancer and 37 patients with prostate cancer were used to construct separate RP models. Additionally, 16 patients from each group (HN and prostate) were selected to assess the performance of Ethos auto-planning results. Initially, a template-based Ethos plan (Non-RP plan) was generated, followed by integrating the corresponding RP model's DVH estimates into the optimization process to generate another plan (RP plan). We compared the target coverage, OAR doses, and total monitor units between the non-RP and RP plans. Both RP and non-RP plans achieved comparable target coverage in HN and Prostate cases, with a negligible difference of less than 0.5% (p > 0.2). RP plans consistently demonstrated lower doses of OARs in both HN and prostate cases. Specifically, the mean doses of OARs were significantly reduced by 9% (p < 0.05). RP plans required slightly higher monitor units in both HN and prostate sites (p < 0.05), however, the plan generation time was almost similar (p > 0.07). The inclusion of the RP model reduced the OAR doses, particularly reducing the mean dose to critical organs compared to non-RP plans while maintaining similar target coverage. Our findings provide valuable insights for clinics adopting Ethos planning, potentially enhancing the auto-planning to operate optimally.
近年来,自动计划在调强放射治疗中越来越受欢迎。利用先前临床知识的见解可以提高自动计划的质量。在这项工作中,我们评估了基于知识引导的Ethos自动计划的性能,特别是使用快速计划(RP)。74例头颈(HN)癌患者和37例前列腺癌患者被用于构建单独的RP模型。此外,从每组(HN和前列腺)中选择16例患者来评估Ethos自动计划结果的性能。最初,生成基于模板的Ethos计划(非RP计划),然后将相应RP模型的剂量体积直方图(DVH)估计值整合到优化过程中以生成另一个计划(RP计划)。我们比较了非RP计划和RP计划之间的靶区覆盖、危及器官(OAR)剂量和总监测单位。在HN和前列腺病例中,RP计划和非RP计划实现了相当的靶区覆盖,差异可忽略不计,小于0.5%(p>0.2)。在HN和前列腺病例中,RP计划始终显示出较低的OAR剂量。具体而言,OAR的平均剂量显著降低了9%(p<0.05)。在HN和前列腺部位,RP计划需要略高的监测单位(p<0.05),然而,计划生成时间几乎相似(p>0.07)。与非RP计划相比,纳入RP模型降低了OAR剂量,特别是降低了对关键器官的平均剂量,同时保持了相似的靶区覆盖。我们的研究结果为采用Ethos计划的临床实践提供了有价值的见解,可能会增强自动计划以实现最佳运行。