Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands.
HollandPTC, Department of Medical Physics & Informatics, Delft, The Netherlands.
Phys Med Biol. 2024 Mar 14;69(7). doi: 10.1088/1361-6560/ad2a98.
. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlinere-planning) schedule, including extensive robustness analyses.. The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlinere-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using polynomial chaos expansion for fast and accurate dose calculations.. For 14/67 repeat-CTs, offlinere-planning resulted in <50% probability of≥ 95% of the prescribed dose () in one or both CTVs, which never happened with online re-optimization. With offlinere-planning, eight repeat-CTs had zero probability of obtaining≥ 95%for CTV, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (< 10for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 min on average.. The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.
在头颈部癌症强度调制质子治疗中，自适应放疗目前仅限于离线重新规划，以减轻患者解剖结构缓慢变化的影响。每日在线自适应调整有可能改善剂量学。这里提出了一种新的完全自动化的在线重新优化策略。在回顾性研究中，将这种在线重新优化方法与我们基于触发的离线重新规划（offlinere-planning）方案进行了比较，包括广泛的稳健性分析。在线重新优化方法采用自动化多标准重新优化，使用具有 1 毫米设置稳健性设置的稳健优化（与 offlinere-planning 的 3 毫米相比）。硬规划约束和点添加用于确保足够的靶区覆盖，避免不可接受的最大剂量，并最大限度地减少危及器官的剂量。对于 15 名患者的 67 次重复 CT，比较了两种策略在 CTV 和危及器官的分次剂量。对于每个重复 CT，使用多项式混沌展开对 10000 个具有不同设置和范围稳健性设置的分数剂量进行模拟，以实现快速准确的剂量计算。对于 14/67 次重复 CT，offlinere-planning 导致一个或两个 CTV 中≥95%的处方剂量的概率<50%（从未发生过在线重新优化）。对于 offlinere-planning，8 次重复 CT 获得 CTV≥95%的概率为零，而在线重新优化的最小概率为 81%。口干和吞咽困难等级≥2 的风险分别降低了 3.5±1.7 和 3.9±2.8 个百分点（均<10）。在在线重新优化中，调整点配置后，点强度重新优化平均需要 3.4 分钟。快速在线重新优化策略始终防止了由于日常解剖变化引起的靶区覆盖的大量损失，而不是基于临床触发的离线重新规划方案。除此之外，在线重新优化可以使用较小的设置稳健性设置进行，有助于更好地保护危及器官。
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