Roberfroid Benjamin, Huet-Dastarac Margerie, Borderías-Villarroel Elena, Koffeing Rodin, Lee John A, Barragán-Montero Ana M, Sterpin Edmond
Université catholique de Louvain - Center of Molecular Imaging, Radiotherapy and Oncology (MIRO) Brussels Belgium.
KU Leuven - Department of Oncology Laboratory of Experimental Radiotherapy Leuven Belgium.
Phys Imaging Radiat Oncol. 2025 Jan 30;33:100705. doi: 10.1016/j.phro.2025.100705. eCollection 2025 Jan.
Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans.
The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: , created de novo, and , generated with the proposed adaptive workflow. Robust optimization was performed for all plans.
plans achieved similar mean dose to organs at risk as the : the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the plans. The median target coverage, i.e. D, to primary tumor and nodes of plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement.
plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.
质子弧形治疗(PAT)是一种新兴的治疗方式,可提供连续旋转的质子束。当前的PAT治疗计划方法耗时较长,不适用于在线调整。本研究提出了一种加速的PAT计划调整工作流程。
所提出的工作流程将初始计算机断层扫描(CT)中的射束点转移至当日的CT图像,根据初始模式更新能量层,并基于射束点的初始权重及其对目标函数的影响对选定的转移射束点进行重新优化。对五名头颈部患者进行了一项回顾性研究,这些患者在重复的CT图像上进行了计划调整。在重复的CT图像上采用两种不同方法生成PAT计划:,从头开始创建,以及,采用所提出的自适应工作流程生成。对所有计划均进行了稳健优化。
计划在危及器官的平均剂量方面与计划相似:下颌骨平均剂量的最大中位数增加为1.9 Gy;计划的脊髓最大剂量中位数比计划低0.5 Gy。在标称情况下,计划对原发肿瘤和淋巴结的中位靶区覆盖度(即D)分别降低了0.2 Gy和0.4 Gy,在最坏情况下分别降低了0.4 Gy和0.6 Gy;所有计划均达到临床目标。智能自适应方法将平均计划时间从19184秒减少至5626秒,提高了3.4倍。
计划与参考方法具有相似的计划质量,同时显著减少了针对新患者解剖结构的计划生成时间。