Abstract：
BACKGROUND: Lattice radiation therapy (LRT) alternates regions of high and low doses within the target. The heterogeneous dose distribution is delivered to a geometrical structure of vertices segmented inside the tumor. LRT is typically used to treat patients with large tumor volumes with cytoreduction intent. Due to the geometric complexity of the target volume and the required dose distribution, LRT treatment planning demands additional resources, which may limit clinical integration.
OBJECTIVE: We introduce a fully automated method to (1) generate an ordered lattice of vertices with various sizes and center-to-center distances and (2) perform dose optimization and calculation. We aim to report the dosimetry associated with these lattices to help clinical decision-making.
METHODS: Sarcoma cancer patients with tumor volume between 100 cm3 and 1500 cm3 who received radiotherapy treatment between 2010 and 2018 at our institution were considered for inclusion. Automated segmentation and dose optimization/calculation were performed by using the Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices were modeled by spheres segmented within the gross tumor volume (GTV) with 1 cm/1.5 cm/2 cm diameters (LRT-1 cm/1.5 cm/2 cm) and 2 to 5 cm center-to-center distance on square lattices alternating along the superior-inferior direction. Organs at risk were modeled by subtracting the GTV from the body structure (body-GTV). The prescription dose was that 50% of the vertice volume should receive at least 20 Gy in one fraction. The automated dose optimization included three stages. The vertices optimization objectives were refined during optimization according to their values at the end of the first and second stages. Lattices were classified according to a score based on the minimization of body-GTV max dose and the maximization of GTV dose uniformity (measured with the equivalent uniform dose [EUD]), GTV dose heterogeneity (measured with the GTV D90%/D10% ratio), and the number of patients with more than one vertex inserted in the GTV. Plan complexity was measured with the modulation complexity score (MCS). Correlations were assessed with the Spearman correlation coefficient (r) and its associated p-value.
RESULTS: Thirty-three patients with GTV volumes between 150 and 1350 cm3 (median GTV volume = 494 cm3 , IQR = 272-779 cm3 were included. The median time required for segmentation/planning was 1 min/21 min. The number of vertices was strongly correlated with GTV volume in each LRT lattice for each center-to-center distance (r > 0.85, p-values < 0.001 in each case). Lattices with center-to-center distance = 2.5 cm/3 cm/3.5 cm in LRT-1.5 cm and center-to-center distance = 4 cm in LRT-1 cm had the best scores. These lattices were characterized by high heterogeneity (median GTV D90%/D10% between 0.06 and 0.19). The generated plans were moderately complex (median MCS ranged between 0.19 and 0.40).
CONCLUSIONS: The automated LRT planning method allows for the efficacious generation of vertices arranged in an ordered lattice and the refinement of planning objectives during dose optimization, enabling the systematic evaluation of LRT dosimetry from various lattice geometries.
OBJECTIVE: We introduce a fully automated method to (1) generate an ordered lattice of vertices with various sizes and center-to-center distances and (2) perform dose optimization and calculation. We aim to report the dosimetry associated with these lattices to help clinical decision-making.
METHODS: Sarcoma cancer patients with tumor volume between 100 cm3 and 1500 cm3 who received radiotherapy treatment between 2010 and 2018 at our institution were considered for inclusion. Automated segmentation and dose optimization/calculation were performed by using the Eclipse Scripting Application Programming Interface (ESAPI, v16, Varian Medical Systems, Palo Alto, USA). Vertices were modeled by spheres segmented within the gross tumor volume (GTV) with 1 cm/1.5 cm/2 cm diameters (LRT-1 cm/1.5 cm/2 cm) and 2 to 5 cm center-to-center distance on square lattices alternating along the superior-inferior direction. Organs at risk were modeled by subtracting the GTV from the body structure (body-GTV). The prescription dose was that 50% of the vertice volume should receive at least 20 Gy in one fraction. The automated dose optimization included three stages. The vertices optimization objectives were refined during optimization according to their values at the end of the first and second stages. Lattices were classified according to a score based on the minimization of body-GTV max dose and the maximization of GTV dose uniformity (measured with the equivalent uniform dose [EUD]), GTV dose heterogeneity (measured with the GTV D90%/D10% ratio), and the number of patients with more than one vertex inserted in the GTV. Plan complexity was measured with the modulation complexity score (MCS). Correlations were assessed with the Spearman correlation coefficient (r) and its associated p-value.
RESULTS: Thirty-three patients with GTV volumes between 150 and 1350 cm3 (median GTV volume = 494 cm3 , IQR = 272-779 cm3 were included. The median time required for segmentation/planning was 1 min/21 min. The number of vertices was strongly correlated with GTV volume in each LRT lattice for each center-to-center distance (r > 0.85, p-values < 0.001 in each case). Lattices with center-to-center distance = 2.5 cm/3 cm/3.5 cm in LRT-1.5 cm and center-to-center distance = 4 cm in LRT-1 cm had the best scores. These lattices were characterized by high heterogeneity (median GTV D90%/D10% between 0.06 and 0.19). The generated plans were moderately complex (median MCS ranged between 0.19 and 0.40).
CONCLUSIONS: The automated LRT planning method allows for the efficacious generation of vertices arranged in an ordered lattice and the refinement of planning objectives during dose optimization, enabling the systematic evaluation of LRT dosimetry from various lattice geometries.
摘要：
背景：点阵放射治疗（LRT）在目标内交替高剂量和低剂量区域。异质剂量分布被递送到在肿瘤内部分割的顶点的几何结构。LRT通常用于治疗具有细胞减少意图的大肿瘤体积的患者。由于目标体积的几何复杂性和所需的剂量分布，轻轨治疗计划需要额外的资源，这可能会限制临床整合。
目的：我们引入了一种全自动方法，以（1）生成具有各种尺寸和中心到中心距离的顶点的有序晶格和（2）进行剂量优化和计算。我们旨在报告与这些晶格相关的剂量学，以帮助临床决策。
方法：考虑纳入2010年至2018年在我们机构接受放射治疗的肿瘤体积在100cm3至1500cm3之间的肉瘤癌症患者。通过使用Eclipse脚本应用程序编程接口（ESAPI，V16，瓦里安医疗系统，帕洛阿尔托,美国）。通过在大体肿瘤体积（GTV）内分割的球体建模，球体直径为1cm/1.5cm/2cm（LRT-1cm/1.5cm/2cm），中心到中心的距离为2至5cm。沿着上下方向交替的正方形晶格。通过从身体结构(body-GTV)中减去GTV来对处于危险中的器官进行建模。处方剂量是50%的顶点体积应在一个部分中接受至少20Gy。自动化剂量优化包括三个阶段。在优化过程中，根据第一阶段和第二阶段结束时的值对顶点优化目标进行了细化。根据身体GTV最大剂量的最小化和GTV剂量均匀性的最大化（用等效均匀剂量[EUD]测量）的评分对晶格进行分类。GTV剂量异质性（用GTVD90%/D10%比率测量），以及在GTV中插入一个以上顶点的患者人数。使用调制复杂度评分(MCS)来测量计划复杂度。用Spearman相关系数(r)及其相关p值评估相关性。
结果：33例GTV体积在150至1350cm3之间的患者（GTV体积中位数=494cm3，包括IQR=272-779cm3。分割/计划所需的中位时间为1分钟/21分钟。对于每个中心到中心距离，每个LRT晶格中的顶点数与GTV体积密切相关（r>0.85，每种情况下p值<0.001）。在LRT-1.5cm中具有中心到中心距离=2.5cm/3cm/3.5cm并且在LRT-1cm中具有中心到中心距离=4cm的格子具有最佳得分。这些晶格的特征在于高异质性(GTVD90%/D10%的中值在0.06和0.19之间)。生成的计划是中等复杂的(中位MCS范围在0.19和0.40之间)。
结论：自动LRT计划方法允许有效地生成排列在有序晶格中的顶点，并在剂量优化期间细化计划目标，能够从各种晶格几何形状对LRT剂量测定进行系统评估。
目的：我们引入了一种全自动方法，以（1）生成具有各种尺寸和中心到中心距离的顶点的有序晶格和（2）进行剂量优化和计算。我们旨在报告与这些晶格相关的剂量学，以帮助临床决策。
方法：考虑纳入2010年至2018年在我们机构接受放射治疗的肿瘤体积在100cm3至1500cm3之间的肉瘤癌症患者。通过使用Eclipse脚本应用程序编程接口（ESAPI，V16，瓦里安医疗系统，帕洛阿尔托,美国）。通过在大体肿瘤体积（GTV）内分割的球体建模，球体直径为1cm/1.5cm/2cm（LRT-1cm/1.5cm/2cm），中心到中心的距离为2至5cm。沿着上下方向交替的正方形晶格。通过从身体结构(body-GTV)中减去GTV来对处于危险中的器官进行建模。处方剂量是50%的顶点体积应在一个部分中接受至少20Gy。自动化剂量优化包括三个阶段。在优化过程中，根据第一阶段和第二阶段结束时的值对顶点优化目标进行了细化。根据身体GTV最大剂量的最小化和GTV剂量均匀性的最大化（用等效均匀剂量[EUD]测量）的评分对晶格进行分类。GTV剂量异质性（用GTVD90%/D10%比率测量），以及在GTV中插入一个以上顶点的患者人数。使用调制复杂度评分(MCS)来测量计划复杂度。用Spearman相关系数(r)及其相关p值评估相关性。
结果：33例GTV体积在150至1350cm3之间的患者（GTV体积中位数=494cm3，包括IQR=272-779cm3。分割/计划所需的中位时间为1分钟/21分钟。对于每个中心到中心距离，每个LRT晶格中的顶点数与GTV体积密切相关（r>0.85，每种情况下p值<0.001）。在LRT-1.5cm中具有中心到中心距离=2.5cm/3cm/3.5cm并且在LRT-1cm中具有中心到中心距离=4cm的格子具有最佳得分。这些晶格的特征在于高异质性(GTVD90%/D10%的中值在0.06和0.19之间)。生成的计划是中等复杂的(中位MCS范围在0.19和0.40之间)。
结论：自动LRT计划方法允许有效地生成排列在有序晶格中的顶点，并在剂量优化期间细化计划目标，能够从各种晶格几何形状对LRT剂量测定进行系统评估。
