Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, 92037, USA.
Med Phys. 2020 Nov;47(11):5419-5427. doi: 10.1002/mp.14476. Epub 2020 Oct 8.
To investigate the utility of gradient dose segmented analysis (GDSA) in combination with in vivo electronic portal imaging device (EPID) images to predict changes in the PTV mean dose for patient cases. Also, we use the GDSA to retrospectively analyze patients treated in our clinic to assess deviations for different treatment sites and use time-series data to observe any day-to-day changes.
In vivo EPID transit images acquired on the Varian Halcyon were analyzed for simulated errors in a phantom, including gas bubbles, weight loss, patient shifts, and an arm erroneously in the field. GDSA threshold parameters were tuned to maximize the coefficient of determination (R ) between GDSA metrics and the change in the PTV mean dose (D ) as estimated in a treatment planning system (TPS). Similarly for a gamma analysis, the gamma criteria were adjusted to maximize R between gamma pass rate and the change in the PTV D from the TPS. The predictive accuracy of these models was tested on patient data measuring the mean and standard deviation of the difference in the predicted change in PTV D and the change in PTV D measured in the TPS. This analysis was extended retrospectively for every patient treated over a 23-month period (n = 852 patients) to assess the range of expected deviations that occurred during routine clinical operation, as well as to assess any differences between treatment sites. Grouping patients treated on the same day, a time-series analysis was performed to determine if GDSA metrics could add value in tracking machine behavior over time.
For the phantom data, analyzing the errors, except for shifts, and comparing the change in PTV D and GDSA mean, a maximal R = 0.90 was found for a dose threshold of 5% and gradient threshold of 3 mm. For the gamma approach a linear fit between the gamma pass rate for change in the PTV D was assessed for different criteria, using the same image data. A maximal, R = 0.84 was found for a gamma criteria of 3%/3 mm, 45% lower dose threshold. For patient data, the predictive accuracy of the change in the PTV D using the GDSA approach and the gamma approach was 0.09 ± 0.98 % and - 0.65 ± 2.21%, respectively. Comparing the two approaches the accuracy did not significantly differ (P = 0.38), whereas the precision of the GDSA prediction is significantly less (P < 0.001). The dosimetric impact of shifts was not detectable with either the GDSA or gamma approach. Analysis of all patients treated over 23 months showed that over 95% of fractions treated deviated from the first fraction by 2% or less. Deviations> 2% occurred most frequently for the later fractions of head-and-neck and lung treatments. Additionally, averaging the GDSA mean metric over all patients on a given treatment day showed that changes in the machine output on the order of 1% could be identified.
GDSA of in vivo EPID images is a useful technique for monitoring patient changes during the course of treatment, particularly weight loss and tumor shrinkage. The GDSA mean provides a quantitative estimate of the change in the PTV D , giving a simple, quantitative metric by which to flag patients with clinically meaningful deviations in treatment. Averaging the GDSA metric over all patients treated on a given day and tracking daily variations can also provide a flag for any systematic deviations in treatment due to machine performance.
研究梯度剂量分段分析（GDSA）与体内电子射野影像装置（EPID）图像相结合在预测患者 PTV 平均剂量变化中的应用。此外，我们使用 GDSA 回顾性分析我们诊所治疗的患者，评估不同治疗部位的偏差，并使用时间序列数据观察任何日常变化。
对瓦里安 Halcyon 上采集的模拟误差的体内 EPID 传输图像进行分析，包括气穴、体重减轻、患者移位以及手臂错误进入射野。调整 GDSA 阈值参数，以最大化 GDSA 指标与治疗计划系统（TPS）中估计的 PTV 平均剂量（D）变化之间的决定系数（R）。同样对于伽马分析，调整伽马标准以最大化伽马通过率与 TPS 中 PTV D 变化之间的 R。在患者数据上测试这些模型的预测准确性，测量预测的 PTV D 变化与 TPS 中测量的 PTV D 变化之间的差异的平均值和标准偏差。这项分析扩展到对过去 23 个月内治疗的每一位患者（n=852 名患者）进行回顾性评估，以评估在常规临床操作中发生的预期偏差范围，并评估治疗部位之间的差异。将同一天治疗的患者分组，进行时间序列分析，以确定 GDSA 指标是否可以随时间跟踪机器行为。
对于体模数据，分析误差，除了移位，并比较 PTV D 和 GDSA 均值的变化，发现剂量阈值为 5%和梯度阈值为 3mm 时，R最大值为 0.90。对于伽马方法，评估了不同标准下 PTV D 变化的伽马通过率之间的线性拟合，使用相同的图像数据。在剂量阈值为 3%和梯度阈值为 3mm，45%的低剂量阈值下，发现伽马通过率的最大值 R 为 0.84。对于患者数据，使用 GDSA 方法和伽马方法预测 PTV D 变化的预测精度分别为 0.09±0.98%和-0.65±2.21%。比较两种方法，精度没有显著差异（P=0.38），但 GDSA 预测的精度显著较低（P<0.001）。GDSA 或伽马方法都无法检测到移位的剂量学影响。对过去 23 个月内治疗的所有患者进行分析表明，超过 95%的治疗分数与第一分数相差 2%或更小。头颈部和肺部治疗的后期分数中，偏差>2%的情况最常见。此外，对特定治疗日的所有患者的 GDSA 平均值进行平均，可以识别出 1%左右的机器输出变化。
体内 EPID 图像的 GDSA 是监测治疗过程中患者变化的有用技术，特别是体重减轻和肿瘤缩小。GDSA 均值提供了 PTV D 变化的定量估计，提供了一个简单的定量指标，用于标记治疗中存在临床意义偏差的患者。对特定治疗日的所有患者的 GDSA 平均值进行平均，并跟踪每日变化，也可以为由于机器性能而导致的任何系统性治疗偏差提供标志。
