Department of Radiotherapy and Radio-Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Med Phys. 2024 Sep;51(9):5890-5900. doi: 10.1002/mp.17304. Epub 2024 Jul 20.
Surrogate-based motion compensation in stereotactic body radiation therapy (SBRT) strongly relies on a constant relationship between an external breathing signal and the internal tumor motion over the course of treatment, that is, a stable patient-specific correspondence model.
This study aims to develop methods for analyzing the stability of correspondence models by integrating planning 4DCT and pretreatment 4D cone-beam computed tomography (4DCBCT) data and assessing the relation to patient-specific clinical parameters.
For correspondence modeling, a regression-based approach is applied, correlating patient-specific internal motion (vector fields computed by deformable image registration) and external breathing signals (recorded by Varian's RPM and RGSC system). To analyze correspondence model stability, two complementary methods are proposed. (1) Target volume-based analysis: 4DCBCT-based correspondence models predict clinical target volumes (GTV and internal target volume [ITV]) within the planning 4DCT, which are evaluated by overlap and distance measures (Dice similarity coefficient [DSC]/average symmetric surface distance [ASSD]). (2) System matrix-based analysis: 4DCBCT-based regression models are compared to 4DCT-based models using mean squared difference (MSD) and principal component analysis of the system matrices. Stability analysis results are correlated with clinical parameters. Both methods are applied to a dataset of 214 pretreatment 4DCBCT scans (Varian TrueBeam) from a cohort of 46 lung tumor patients treated with ITV-based SBRT (planning 4DCTs acquired with Siemens AS Open and SOMATOM go.OPEN Pro CT scanners).
Consistent results across the two complementary analysis approaches (Spearman correlation coefficient of between system matrix-based MSD and GTV-based DSC/ASSD) were observed. Analysis showed that stability was not predominant, with 114/214 fraction-wise models not surpassing a threshold of for the GTV, and only 14/46 patients demonstrating a in all fractions. Model stability did not degrade over the course of treatment. The mean GTV-based DSC is (mean ASSD of ) and the respective ITV-based DSC is (mean ASSD of ). The clinical parameters showed a strong correlation between smaller tumor motion ranges and increased stability.
The proposed methods identify patients with unstable correspondence models prior to each treatment fraction, serving as direct indicators for the necessity of replanning and adaptive treatment approaches to account for internal-external motion variations throughout the course of treatment.
立体定向体部放射治疗（SBRT）中的基于代理的运动补偿强烈依赖于治疗过程中外部呼吸信号与内部肿瘤运动之间的恒定关系，即稳定的患者特异性对应模型。
本研究旨在通过整合计划 4DCT 和预处理 4D 锥形束计算机断层扫描（4DCBCT）数据来开发分析对应模型稳定性的方法，并评估与患者特异性临床参数的关系。
对于对应建模，应用基于回归的方法，将患者特异性内部运动（由变形图像配准计算的向量场）与外部呼吸信号相关联（由瓦里安的 RPM 和 RGSC 系统记录）。为了分析对应模型的稳定性，提出了两种互补的方法。（1）基于目标体积的分析：基于 4DCBCT 的对应模型预测计划 4DCT 中的临床靶区（GTV 和内部靶区 [ITV]），通过重叠和距离测量（Dice 相似性系数 [DSC]/平均对称表面距离 [ASSD]）进行评估。（2）基于系统矩阵的分析：使用均方差（MSD）和系统矩阵的主成分分析比较基于 4DCBCT 的回归模型和基于 4DCT 的模型。稳定性分析结果与临床参数相关。这两种方法均应用于来自 46 例 ITV 为基础的 SBRT 治疗的肺肿瘤患者队列的 214 例预处理 4DCBCT 扫描（瓦里安 TrueBeam）数据集（计划 4DCT 使用西门子 AS Open 和 SOMATOM go.OPEN Pro CT 扫描仪采集）。
两种互补分析方法的结果一致（系统矩阵基于 MSD 与基于 GTV 的 DSC/ASSD 的 Spearman 相关系数为 ）。分析表明，稳定性并不占主导地位，114/214 个分数模型没有超过 GTV 的阈值，并且只有 14/46 个患者在所有分数中都有 。模型稳定性在治疗过程中没有下降。基于 GTV 的平均 DSC 为 （平均 ASSD 为 ），相应的 ITV 基于 DSC 为 （平均 ASSD 为 ）。临床参数显示肿瘤运动范围越小，稳定性越高。
提出的方法在每个治疗分数之前识别出对应模型不稳定的患者，作为在治疗过程中需要重新计划和自适应治疗方法以考虑内外运动变化的直接指标。
