AI 中文总结
研究针对神经肿瘤患者，比较概率计划与稳健计划，采用基于百分位数的概率方法，通过嵌套结构和多项式混沌展开采样计算，实现更好的靶区覆盖与危及器官剂量降低权衡，优化时间有改善。
AI 中文摘要
为了展示先前提出的概率规划方法在神经肿瘤患者群体中针对临床目标进行精确优化的临床可行性，我们将概率计划与（自动化）稳健计划进行了比较。概率方法基于百分位数，利用百分位数可近似为其期望值和标准差的线性组合这一事实。优化具有嵌套结构，通过对剂量影响矩阵的多项式混沌展开进行采样来高效计算百分位数。结果表明，该方法在靶区覆盖和危及器官剂量降低之间实现了更好的权衡，可能带来更好的治疗效果。
英文摘要
To show clinical feasibility of a previously proposed probabilistic planning approach that can precisely optimize for clinical goals with patient-specific acceptance probabilities on a neuro-oncological patient group, we compared probabilistic plans with (automated) robust plans for one patient (group A) that could achieve sufficient clinical target coverage and for four patients (group B) where target coverage had to be compromised due to organ-at-risk (OAR) dose constraints. The probabilistic approach is percentile-based and uses the fact that a (dose) percentile can be approximated as a linear combination of its expected value and standard deviation. The optimization has a nested structure: the inner optimization optimizes the beam weights for a given percentile estimate, while an outer loop iteratively updates and improves the accuracy of the percentile estimate. For every outer iteration, the optimization is warm-started from the previous iteration. Percentiles are efficiently calculated by sampling a polynomial chaos expansion of the dose-influence matrix. The patient in group A achieved cumulative OAR dose reductions (of OAR-related DVH-metrics) of 19 GyRBE, for identical target coverage. Target coverage improved for all patients in group B (the 10th percentile of $D_{99.8\%}$ increased up to 0.93 GyRBE), at the same time reaching cumulative OAR dose reductions (of OAR-related DVH-metrics) up to 33 GyRBE. Probabilistic plans were optimized in 44h to 141h. For two representative patients, eliminating warm-starting (i.e., the outer loop) from the approach reduced total optimization times to below 10h (which took originally 80h and 141h). Compared to robust optimization methods, the probabilistic approach achieves improved trade-offs between probabilistic target coverage and OAR sparing, potentially leading to better treatments.
