Chen Shupeng, Qin An, Yan Di
Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, United States.
Radiation Oncology, Huaxi Hospital/School of Medicine, Chengdu, China.
Front Oncol. 2022 Jul 6;12:876861. doi: 10.3389/fonc.2022.876861. eCollection 2022.
Tumor voxel dose-response matrix (DRM) can be quantified using feedback from serial FDG-PET/CT imaging acquired during radiotherapy. This study investigated the dynamic characteristics and the predictive capability of DRM.
FDG-PET/CT images were acquired before and weekly during standard chemoradiotherapy with the treatment dose 2 Gy × 35 from 31 head and neck cancer patients. For each patient, deformable image registration was performed between the pretreatment/baseline PET/CT image and each weekly PET/CT image. Tumor voxel DRM was derived using linear regression on the logarithm of the weekly standard uptake value (SUV) ratios for each tumor voxel, such as SUV measured at a dose level normalized to the baseline SUV. The dynamic characteristics were evaluated by comparing the DRM estimated using a single feedback image acquired at the th treatment week ( = 1, 2, 3, or 4) to the DRM estimated using the last feedback image for each patient. The predictive capability of the DRM estimated using 1 or 2 feedback images was evaluated using the receiver operating characteristic test with respect to the treatment outcome of tumor local-regional control or failure.
The mean ± SD of tumor voxel SUV measured at the pretreatment and the 1st, 2nd, 3rd, 4th, and last treatment weeks was 6.76 ± 3.69, 5.72 ± 3.43, 3.85 ± 2.22, 3.27 ± 2.25, 2.5 ± 1.79, and 2.23 ± 1.27, respectively. The deviations between the DRM estimated using the single feedback image obtained at the th week and the last feedback image were 0.86 ± 4.87, -0.06 ± 0.3, -0.09 ± 0.17, and -0.09 ± 0.12 for DRM, DRM, DRM, and DRM, respectively. The predictive capability of DRM and DRM was significant (p < 0.001). The area under the curve (AUC) was increased with the increase in treatment dose level. The DRMs constructed using the single feedback image achieved an AUC of 0.861. The AUC was slightly improved to 0.941 for the DRMs estimated using 2 feedback images.
Tumor voxel metabolic activity measured using FDG-PET/CT fluctuated noticeably during the first 2 treatment weeks and obtained a stabilized reduction rate thereafter. Tumor voxel DRM constructed using a single FDG-PET/CT feedback image after the 2nd treatment week (>20 Gy) has a good predictive capability. The predictive capability improved continuously using a later feedback image and marginally improved when two feedback images were applied.
肿瘤体素剂量反应矩阵(DRM)可通过放疗期间获取的系列FDG-PET/CT成像的反馈进行量化。本研究调查了DRM的动态特征和预测能力。
对31例头颈部癌患者在标准放化疗前及放疗期间每周进行FDG-PET/CT成像,治疗剂量为2 Gy×35。对于每位患者,在治疗前/基线PET/CT图像与每周的PET/CT图像之间进行可变形图像配准。肿瘤体素DRM通过对每个肿瘤体素每周标准摄取值(SUV)比值的对数进行线性回归得出,例如在归一化至基线SUV的剂量水平下测量的SUV。通过将使用第t治疗周(t = 1、2、3或4)获取的单个反馈图像估计的DRM与使用每位患者的最后一个反馈图像估计的DRM进行比较,评估动态特征。使用1或2个反馈图像估计的DRM的预测能力通过关于肿瘤局部区域控制或失败的治疗结果的受试者操作特征测试进行评估。
在治疗前、第1、2、3、4和最后治疗周测量的肿瘤体素SUV的平均值±标准差分别为6.76±3.69、5.72±3.43、3.85±2.22、3.27±2.25、2.5±1.79和2.23±1.27。使用第t周获得的单个反馈图像估计的DRM与最后一个反馈图像之间的偏差,对于DRM1、DRM2、DRM3和DRM4分别为0.86±4.87、-0.06±0.3、-0.09±0.17和-(此处原文似乎有误,推测应为-0.09±0.12)。DRM1和DRM2的预测能力显著(p < 0.001)。曲线下面积(AUC)随治疗剂量水平的增加而增加。使用单个反馈图像构建的DRM的AUC为0.861。对于使用2个反馈图像估计的DRM,AUC略有提高至0.941。
使用FDG-PET/CT测量的肿瘤体素代谢活性在治疗的前2周内波动明显,此后获得稳定的降低率。在第2治疗周(>20 Gy)后使用单个FDG-PET/CT反馈图像构建的肿瘤体素DRM具有良好的预测能力。使用较晚的反馈图像预测能力持续提高,应用两个反馈图像时略有改善。