School of Mathematics and Statistics, Wuhan University, Wuhan, China.
Department of Radiation Oncology, Stanford University, Stanford, California, USA.
Med Phys. 2024 Sep;51(9):6220-6230. doi: 10.1002/mp.17285. Epub 2024 Jul 5.
Intensity-modulated proton therapy (IMPT) optimizes spot intensities and position, providing better conformability. However, the successful application of IMPT is dependent upon addressing the challenges posed by range and setup uncertainties. In order to address the uncertainties in IMPT, robust optimization is essential.
This study aims to develop a novel fast algorithm for robust optimization of IMPT with minimum monitor unit (MU) constraint.
The study formulates a robust optimization problem and proposes a novel, fast algorithm based on the alternating direction method of multipliers (ADMM) framework. This algorithm enables distributed computation and parallel processing. Ten clinical cases were used as test scenarios to evaluate the performance of the proposed approach. The robust optimization method (RBO-NEW) was compared with plans that only consider nominal optimization using CTV (NMO-CTV) without handling uncertainties and PTV (NMO-PTV) to handle the uncertainties, as well as with conventional robust-optimized plans (RBO-CONV). Dosimetric metrics, including D95, homogeneity index, and Dmean, were used to evaluate the dose distribution quality. The area under the root-mean-square dose (RMSD)-volume histogram curves (AUC) and dose-volume histogram (DVH) bands were used to evaluate the robustness of the treatment plan. Optimization time cost was also assessed to measure computational efficiency.
The results demonstrated that the RBO plans exhibited better plan quality and robustness than the NMO plans, with RBO-NEW showing superior computational efficiency and plan quality compared to RBO-CONV. Specifically, statistical analysis results indicated that RBO-NEW was able to reduce the computational time from to s ( ) and reduce the mean organ-at-risk (OAR) dose from % of the prescription dose to % of the prescription dose ( ) compared to RBO-CONV.
This study introduces a novel fast robust optimization algorithm for IMPT treatment planning with minimum MU constraint. Such an algorithm is not only able to enhance the plan's robustness and computational efficiency without compromising OAR sparing but also able to improve treatment plan quality and reliability.
调强质子治疗（IMPT）通过优化射野内强度分布和位置，提高适形度。然而，成功应用 IMPT 取决于解决射程和设置不确定性带来的挑战。为了解决 IMPT 中的不确定性，稳健优化至关重要。
本研究旨在开发一种新的快速算法，用于具有最小监测单元（MU）约束的 IMPT 稳健优化。
该研究制定了一个稳健优化问题，并提出了一种基于交替方向乘子法（ADMM）框架的新快速算法。该算法支持分布式计算和并行处理。使用十个临床病例作为测试场景来评估所提出方法的性能。将稳健优化方法（RBO-NEW）与仅考虑名义优化的计划（CTV 无不确定性处理的 NMO-CTV 和 PTV 有不确定性处理的 NMO-PTV）以及传统稳健优化计划（RBO-CONV）进行比较。使用剂量学指标，包括 D95、均匀性指数和 Dmean，评估剂量分布质量。使用均方根剂量（RMSD）-体积直方图曲线下面积（AUC）和剂量-体积直方图（DVH）带评估治疗计划的稳健性。还评估了优化时间成本，以衡量计算效率。
结果表明，RBO 计划的质量和稳健性优于 NMO 计划，RBO-NEW 的计算效率和计划质量均优于 RBO-CONV。具体而言，统计分析结果表明，与 RBO-CONV 相比，RBO-NEW 能够将计算时间从 减少到 s（ ），并将平均器官风险（OAR）剂量从处方剂量的 %减少到 %（ ）。
本研究提出了一种新的快速稳健优化算法，用于具有最小 MU 约束的 IMPT 治疗计划。这种算法不仅能够在不影响 OAR 保护的情况下提高计划的稳健性和计算效率，还能够提高治疗计划的质量和可靠性。
