Abstract：
OBJECTIVE: The combined effect of hyperthermia and radiation therapy can be quantified by an enhanced equivalent radiation dose (EQDRT). Uncertainties in hyperthermia treatment planning and adjustments during treatment can impact achieved EQDRT. We developed and compared strategies for EQDRT optimization of radiation therapy plans, focusing on robustness against common adjustments.
METHODS: Using Plan2Heat, we computed preplanning hyperthermia plans and treatment adjustment scenarios for 3 cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with 4 different strategies: (1) conventional radiation therapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) nominal EQDRT optimization using the preplanning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk doses as in plan 1, (3) robust EQDRT optimization, as plan 2 but adding adjusted scenarios for optimization, and (4) library of plans (4 plans) with strategy 2 criteria but optimizing on 1 adjusted scenario per plan. We calculated for each radiation therapy plan EQDRT distributions for preplanning and adjusted scenarios, evaluating each combination of GTV coverage and homogeneity objectives.
RESULTS: EQDRT95% increased from 49.9 to 50.9 Gy in strategy 1 to 56.1 to 57.4 Gy in strategy 2 with the preplanning scenario, improving homogeneity by ∼10%. Strategy 2 demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy 3 had a higher percentage of coverage objectives within tolerance than strategy 2 (68% vs 54%) but a lower percentage for uniformity (44% vs 71%). Strategy 4 showed a similar EQDRT95% and homogeneity for adjusted scenarios than strategy 2 for a preplanning scenario. D0.1% (radiation dose received by the 0.1% most irradiated volume) for organs at risk was increased by strategies 2 to 4 by up to ∼6 Gy.
CONCLUSIONS: EQDRT optimization enhances EQDRT levels and uniformity compared with conventional optimization. Better overall robustness is achieved by optimizing the preplanning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.
METHODS: Using Plan2Heat, we computed preplanning hyperthermia plans and treatment adjustment scenarios for 3 cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with 4 different strategies: (1) conventional radiation therapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) nominal EQDRT optimization using the preplanning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk doses as in plan 1, (3) robust EQDRT optimization, as plan 2 but adding adjusted scenarios for optimization, and (4) library of plans (4 plans) with strategy 2 criteria but optimizing on 1 adjusted scenario per plan. We calculated for each radiation therapy plan EQDRT distributions for preplanning and adjusted scenarios, evaluating each combination of GTV coverage and homogeneity objectives.
RESULTS: EQDRT95% increased from 49.9 to 50.9 Gy in strategy 1 to 56.1 to 57.4 Gy in strategy 2 with the preplanning scenario, improving homogeneity by ∼10%. Strategy 2 demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy 3 had a higher percentage of coverage objectives within tolerance than strategy 2 (68% vs 54%) but a lower percentage for uniformity (44% vs 71%). Strategy 4 showed a similar EQDRT95% and homogeneity for adjusted scenarios than strategy 2 for a preplanning scenario. D0.1% (radiation dose received by the 0.1% most irradiated volume) for organs at risk was increased by strategies 2 to 4 by up to ∼6 Gy.
CONCLUSIONS: EQDRT optimization enhances EQDRT levels and uniformity compared with conventional optimization. Better overall robustness is achieved by optimizing the preplanning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.
摘要：
背景：可以通过增强的等效辐射剂量（EQDRT）来量化热疗和放射治疗的联合作用。热疗治疗计划的不确定性和治疗期间的调整可能会影响实现的EQDRT。我们开发并比较了放疗计划的EQDRT优化策略，注重对常见调整的稳健性。
方法：使用Plan2Heat，我们计算了3例宫颈癌患者的计划前热疗计划和治疗调整方案.我们将这些场景导入到RayStation12A中，以四种不同的策略进行优化：（1）常规放射治疗优化规定46Gy到计划目标体积（PTV），（2）使用预规划方案的标称EQDRT优化，在总肿瘤体积（GTV）中靶向均匀58Gy，按照计划（1）保持器官处于危险状态（OAR）剂量，(3)稳健的EQDRT优化，如（2），但添加调整后的方案进行优化，(4)计划库(四个计划)，符合策略(2)标准，但在每个计划的一个调整方案上进行优化。我们为每个放射治疗计划计算了预先计划和调整方案的EQDRT分布，评估每个组合的GTV覆盖率和同质性目标。
结果：EQDRT95％从策略（1）中的49.9-50.9Gy增加到策略（2）中的56.1-57.4Gy，均匀性提高10%。策略(2)表现出最佳的整体稳健性，所有GTV目标的62%在公差范围内。策略（3）在容忍度内的覆盖率目标高于策略（2）（68％vs54％），但均匀性百分比较低(44%vs71%)。策略（4）在调整后的方案中显示出与计划前方案的策略（2）相似的EQDRT95%和同质性。OAR的D0.1%通过策略(2-4)增加了高达6Gy。
结论：与常规优化相比，EQDRT优化提高了EQDRT水平和均匀性。在预先规划的热疗计划上优化实现了更好的整体鲁棒性。稳健优化提高了覆盖率，但降低了均匀性。计划库可确保在处理调整后的热疗方案时的覆盖范围和均匀性。
方法：使用Plan2Heat，我们计算了3例宫颈癌患者的计划前热疗计划和治疗调整方案.我们将这些场景导入到RayStation12A中，以四种不同的策略进行优化：（1）常规放射治疗优化规定46Gy到计划目标体积（PTV），（2）使用预规划方案的标称EQDRT优化，在总肿瘤体积（GTV）中靶向均匀58Gy，按照计划（1）保持器官处于危险状态（OAR）剂量，(3)稳健的EQDRT优化，如（2），但添加调整后的方案进行优化，(4)计划库(四个计划)，符合策略(2)标准，但在每个计划的一个调整方案上进行优化。我们为每个放射治疗计划计算了预先计划和调整方案的EQDRT分布，评估每个组合的GTV覆盖率和同质性目标。
结果：EQDRT95％从策略（1）中的49.9-50.9Gy增加到策略（2）中的56.1-57.4Gy，均匀性提高10%。策略(2)表现出最佳的整体稳健性，所有GTV目标的62%在公差范围内。策略（3）在容忍度内的覆盖率目标高于策略（2）（68％vs54％），但均匀性百分比较低(44%vs71%)。策略（4）在调整后的方案中显示出与计划前方案的策略（2）相似的EQDRT95%和同质性。OAR的D0.1%通过策略(2-4)增加了高达6Gy。
结论：与常规优化相比，EQDRT优化提高了EQDRT水平和均匀性。在预先规划的热疗计划上优化实现了更好的整体鲁棒性。稳健优化提高了覆盖率，但降低了均匀性。计划库可确保在处理调整后的热疗方案时的覆盖范围和均匀性。
