Department of Radiation Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
Med Phys. 2019 Jan;46(1):45-55. doi: 10.1002/mp.13265. Epub 2018 Nov 16.
To assess the sensitivity of various EPID dosimetry alert indicators to patient-related variations and to determine alert threshold values that ensure excellent error detectability.
Our virtual dose reconstruction method uses in air EPID measurements to calculate virtual 3D dose distributions within a CT data set. Patient errors are introduced by transforming the plan-CT into an error-CT data set. Virtual patient dose distributions reconstructed using the plan-CT and the error-CT data set are compared to the planned dose distributions by γ(3%/3 mm) and DVH analysis using seven indicators: ΔD , γ-mean, near γ-max, γ-pass rate, ΔPTV , ΔPTV and ΔPTV . Translation and rotation patient setup errors and uniform contour changes are studied for 104 VMAT plans of 4 treatment sites. Lung expansions and contractions to simulate changes in lung density are considered for 26 IMRT lung plans. A ROC curve is generated for each combination of error and indicator. For each ROC curve, the AUC value and the optimal alert threshold value of the indicator are determined.
AUC values for γ-indicators and ΔPTV are consistently higher than for ΔD and ΔPTV . For VMAT plans, error detectability to patient position shifts is worse for pelvic treatments and best for head-and-neck and brain plans. Excellent detectability is observed for 5 mm translations in head-and-neck plans (AUC = 0.94) and for 4° rotations in brain plans (AUC = 0.89). All sites but prostate show good-to-excellent detectability (AUC > 0.8) for 10 mm translations and 8° rotations and excellent detectability (AUC > 0.9) for ±6 mm patient contour changes. For head-and-neck, excellent detectability is obtained with γ-mean and γ-pass rate threshold values of around 0.63 and 83%, respectively. For brain and rectum, these threshold values are 0.53 and 90%, respectively. In IMRT lung plans, expansions of 3 mm and contractions of 6 mm are detected (AUC > 0.8).
By combining virtual dose reconstructions with synthetic patient data, we developed a framework to assess the sensitivity of our 3D EPID transit dosimetry method to patient-related variations. The detectability of each introduced error is specific to the treatment site and indicator used. Optimal alert criteria can be determined to ensure excellent detectability for each combination of error type and indicator. The alert threshold values and the magnitude of the error that can be detected are site-specific. In situations where the minimum error that can be detected is larger than the clinically desirable action level, EPID transit dosimetry must be used in combination with IGRT procedures to ensure correct patient positioning and early detection of anatomy variations.
评估各种 EPID 剂量学报警指标对患者相关变化的敏感性，并确定确保良好错误检测能力的报警阈值。
我们的虚拟剂量重建方法使用空气 EPID 测量来计算 CT 数据集内的虚拟 3D 剂量分布。通过将计划 CT 转换为误差 CT 数据集来引入患者误差。使用计划 CT 和误差 CT 数据集重建的虚拟患者剂量分布与计划剂量分布进行比较，使用七个指标进行 γ（3%/3mm）和 DVH 分析：ΔD、γ-均值、近 γ-最大值、γ-通过率、ΔPTV、ΔPTV 和 ΔPTV。研究了 4 个治疗部位的 104 个 VMAT 计划的平移和旋转患者设置误差以及均匀轮廓变化。为了模拟肺密度变化，对 26 个 IMRT 肺计划进行了肺膨胀和收缩。为每个误差和指标组合生成 ROC 曲线。对于每个 ROC 曲线，确定指标的 AUC 值和最佳报警阈值。
γ 指标和 ΔPTV 的 AUC 值始终高于 ΔD 和 ΔPTV。对于 VMAT 计划，骨盆治疗的患者位置偏移的错误检测能力较差，头颈部和脑部计划的检测能力最好。在头颈部计划中，5mm 平移的检测能力极好（AUC=0.94），脑部计划中 4°旋转的检测能力极好（AUC=0.89）。所有部位（前列腺除外）均能很好地检测到 10mm 平移和 8°旋转以及±6mm 患者轮廓变化（AUC>0.8）。对于头颈部，γ-均值和 γ-通过率的阈值分别约为 0.63 和 83%，可以获得极好的检测能力。对于脑部和直肠，这些阈值分别为 0.53 和 90%。在 IMRT 肺计划中，检测到 3mm 扩张和 6mm 收缩（AUC>0.8）。
通过将虚拟剂量重建与合成患者数据相结合，我们开发了一种评估我们的 3D EPID 传输剂量学方法对患者相关变化的敏感性的框架。引入的每个误差的检测能力特定于治疗部位和使用的指标。可以确定最佳的报警标准，以确保每种误差类型和指标组合的检测能力良好。报警阈值和可以检测到的误差幅度是特定于部位的。在可以检测到的最小误差大于临床期望的动作水平的情况下，必须将 EPID 传输剂量学与 IGRT 程序结合使用，以确保患者定位正确，并及早发现解剖变化。
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