Abstract：
OBJECTIVE: Delineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer.
METHODS: Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison.
RESULTS: Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient.
CONCLUSIONS: Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the \'dose range\' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.
METHODS: Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison.
RESULTS: Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient.
CONCLUSIONS: Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the \'dose range\' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.
摘要：
目的：轮廓变化和器官运动在目标和危险器官的计划辐射剂量中产生难以量化的不确定性。类似于手动轮廓，大多数自动分割工具为每个结构生成单个轮廓；然而，这并不表明临床上可接受的轮廓范围.这项研究开发了一种生成一系列自动心脏结构分割的方法，结合运动和轮廓不确定性，并评估剂量学对肺癌的影响。
方法：使用本地开发的自动分割工具描绘了18个心脏结构。应用于27例治愈性（计划剂量≥50Gy）的肺癌计划CTs，和轮廓变化是通过使用十个映射图来提供单独的子结构分割来估计的。通过对具有4D计划CT的9/27例患者的10个呼吸阶段的自动轮廓结构来估计与运动相关的心脏分割变化。生成包含这些变化的剂量体积直方图(DVH)用于比较。
结果：平均剂量的变化（Dmean），定义为十个可行的自动分割的值范围，计算每个心脏亚结构。在研究队列中，描绘不确定性和运动的中位数变化分别为2.20-11.09Gy和0.72-4.06Gy，分别。作为相对值，圈定不确定性和运动的Dmean变化在18.7%-65.3%和7.8%-32.5%之间，分别。剂量根据个人计划的剂量分布而变化，不仅仅是分割差异，对位于陡峭剂量梯度区域内的心脏结构具有较大的剂量变化。
结论：使用心脏子结构自动分割工具对描绘变化和呼吸相关心脏运动的放射治疗剂量不确定性进行量化。这预测了“剂量范围”，其中结构的剂量最有可能下降，而不是单DVH曲线。这使得能够在心脏毒性研究和未来计划优化中考虑这些不确定性。这个工具是为心脏结构设计的，但类似的方法可能适用于其他OAR。
方法：使用本地开发的自动分割工具描绘了18个心脏结构。应用于27例治愈性（计划剂量≥50Gy）的肺癌计划CTs，和轮廓变化是通过使用十个映射图来提供单独的子结构分割来估计的。通过对具有4D计划CT的9/27例患者的10个呼吸阶段的自动轮廓结构来估计与运动相关的心脏分割变化。生成包含这些变化的剂量体积直方图(DVH)用于比较。
结果：平均剂量的变化（Dmean），定义为十个可行的自动分割的值范围，计算每个心脏亚结构。在研究队列中，描绘不确定性和运动的中位数变化分别为2.20-11.09Gy和0.72-4.06Gy，分别。作为相对值，圈定不确定性和运动的Dmean变化在18.7%-65.3%和7.8%-32.5%之间，分别。剂量根据个人计划的剂量分布而变化，不仅仅是分割差异，对位于陡峭剂量梯度区域内的心脏结构具有较大的剂量变化。
结论：使用心脏子结构自动分割工具对描绘变化和呼吸相关心脏运动的放射治疗剂量不确定性进行量化。这预测了“剂量范围”，其中结构的剂量最有可能下降，而不是单DVH曲线。这使得能够在心脏毒性研究和未来计划优化中考虑这些不确定性。这个工具是为心脏结构设计的，但类似的方法可能适用于其他OAR。
