Oud Michelle, Breedveld Sebastiaan, Ng Wei Siang Kelvin, Cassetta Roberto, Habraken Steven, Perkó Zoltán, Heijmen Ben, Hoogeman Mischa
Erasmus MC Cancer Institute, Department of Radiation Oncology, Rotterdam, the Netherlands.
HollandPTC, Delft, the Netherlands.
Phys Imaging Radiat Oncol. 2025 Mar 20;34:100752. doi: 10.1016/j.phro.2025.100752. eCollection 2025 Apr.
In online-adaptive proton therapy planning based on cone beam computed tomography (CBCT), CT number errors can pose challenges. We propose an approach for coping with CT number uncertainties by increasing range robustness settings (RRS) in online-adaptive planning. This was compared to our trigger-based offline (TB-Offline) adaptive approach, and to daily replanning using in-room CT-on-rails (CTOR).
For 23 head-and-neck cancer patients, a CTOR and CBCT were acquired in a single fraction. CTOR contours were copied rigidly onto the CBCT. CBCT-based plans were generated with 3, 6, 8, 10, and 12 % RRS, each with 1 mm setup-RS, followed by a forward dose calculation on the reference CTOR. This was compared to dose distributions from our TB-Offline approach (3 mm/3% SRS/RRS), also recomputed on the CTOR. Coverage (voxelwise-minimum) of the primary clinical target volume (CTV) and elective lymph nodes (CTV) and grade ≥ II normal tissue complication probabilities were compared between strategies.
When going from RRS = 3 % to RRS = 10 %, the population 90th percentiles of CTV V improved from 89.6 % to 96.4 %, and CTV V from 92.8 % to 96.4 %. Substantial coverage loss (V<95 %) with CBCT-based online adaptive and RRS = 10 % was observed in 1/23 evaluated patients for CTV and 2/23 for CTV. This was an improvement compared to 3/23 and 4/23 with TB-Offline. Moreover, for RRS = 10 % the average risk of xerostomia improved by 2.4 percentage point compared to TB-Offline.
Robust optimization with increased range robustness settings effectively mitigated dose degradation from CT number errors in CBCT-based online-adaptive proton therapy.
在基于锥形束计算机断层扫描(CBCT)的在线自适应质子治疗计划中,CT值误差可能带来挑战。我们提出一种通过增加在线自适应计划中的射程稳健性设置(RRS)来应对CT值不确定性的方法。将其与我们基于触发的离线(TB-Offline)自适应方法以及使用室内CT导轨(CTOR)进行的每日重新计划进行比较。
对于23例头颈癌患者,在单次分割中采集CTOR和CBCT。将CTOR轮廓刚性复制到CBCT上。基于CBCT的计划分别采用3%、6%、8%、10%和12%的RRS生成,每种RRS均设置1mm的摆位-RS,随后在参考CTOR上进行正向剂量计算。将其与我们的TB-Offline方法(3mm/3% SRS/RRS)的剂量分布进行比较,同样在CTOR上重新计算。比较各策略之间主要临床靶区体积(CTV)和选择性淋巴结(CTV)的覆盖情况(体素最小覆盖)以及≥II级正常组织并发症概率。
当RRS从3%增加到10%时,CTV V的人群第90百分位数从89.6%提高到96.4%,CTV V从92.8%提高到96.4%。在1/23例评估患者的CTV和2/23例患者的CTV中,观察到基于CBCT的在线自适应且RRS = 10%时出现显著的覆盖损失(V<95%)。与TB-Offline方法中3/23和4/23的情况相比,这是一种改善。此外,对于RRS = 10%,与TB-Offline方法相比,口干的平均风险降低了2.4个百分点。
增加射程稳健性设置的稳健优化有效地减轻了基于CBCT的在线自适应质子治疗中CT值误差导致的剂量降级。