Purpose. The purpose of the current study was to investigate the impact of RayStation analytical pencil beam (APB) and Monte Carlo (MC) algorithms on the interplay effect in pencil beam scanning (PBS) proton-based stereotactic body radiation therapy (SBRT) lung plans. Methods. The current in-silico planning study was designed for a total dose of 5000 cGy(RBE) with a fractional dose of 1000 cGy(RBE). First, three sets of nominal plans were generated for each patient: (a) APB optimization followed by APB dose calculation (PB‐PB), (b) APB optimization followed by MC dose calculation (PB‐MC), and (c) MC optimization followed by MC dose calculation (MC‐MC). Second, for each patient, two sets of volumetric repainting plans (five repaintings) – PB-MC_VR5 and MC-MC_VR5 were generated based on PB-MC and MC-MC, respectively. Dosimetric differences between APB and MC algorithms were calculated on the nominal and interplay dose-volume-histograms (DVHs). Results. Interplay evaluation in non-volumetric repainting plans showed that APB algorithm overestimated the target coverage by up to 8.4% for D_95% and 10.5% for D_99%, whereas in volumetric repainting plans, APB algorithm overestimated by up to 5.3% for D_95% and 7.0% for D_99%. Interplay results for MC calculations showed a decrease in D_95% and D_99% by average differences of 3.5% and 4.7%, respectively, in MC‐MC plans and by 1.8% and 3.0% in MC-MC_VR5 plans. Conclusion. In PBS proton-based SBRT lung plans, the combination of APB algorithm and interplay effect reduced the target coverage. This may result in inferior local control. The use of MC algorithm for both optimization and final dose calculations in conjunction with the volumetric repainting technique yielded superior target coverage.

目的。本研究的目的是研究 RayStation 分析笔形束 (APB) 和蒙特卡罗 (MC) 算法对笔形束扫描 (PBS) 基于质子的立体定向身体放射治疗 (SBRT) 肺计划中相互作用的影响。方法。当前的计算机内规划研究设计为总剂量为 5000 cGy(RBE)，部分剂量为 1000 cGy(RBE)。首先，为每位患者生成三组标称计划：（a）APB 优化后进行 APB 剂量计算（PB-PB），（b）APB 优化后进行 MC 剂量计算（PB-MC），以及（c）MC优化后进行 MC 剂量计算 (MC-MC)。二、对于每位患者，两套体积重绘计划（五次重绘）——PB-MC _VR5和 MC-MC _VR5分别基于 PB-MC 和 MC-MC 生成。APB 和 MC 算法之间的剂量学差异是在标称和相互作用剂量体积直方图 (DVH) 上计算的。结果。在非容积重绘计划相互作用评价结果表明，APB算法高估达目标覆盖至8.4％为d _95％为d和10.5％_99％ ，而在体积重绘计划，APB算法高估高达5.3％为d _{95 %}和 7.0% 为 D _99%。MC 计算的相互作用结果显示 D _95%和 D _{99% 下降}MC-MC 计划的平均差异分别为 3.5% 和 4.7%，MC-MC _VR5计划的平均差异分别为 1.8% 和 3.0% 。结论。在基于 PBS 质子的 SBRT 肺计划中，APB 算法和相互作用的结合降低了目标覆盖率。这可能导致较差的局部控制。将 MC 算法用于优化和最终剂量计算并结合体积重绘技术产生了卓越的目标覆盖率。