Bradshaw Tyler, Fu Rau, Bowen Stephen, Zhu Jun, Forrest Lisa, Jeraj Robert
Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53705-2275, USA.
Phys Med Biol. 2015 Jul 7;60(13):5211-24. doi: 10.1088/0031-9155/60/13/5211. Epub 2015 Jun 17.
Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R(2) and pseudo R(2) were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R(2) ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R(2) = 0.31), but there was still large variability between patients in R(2). The R(2) from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.
剂量描绘依赖于功能成像识别耐药肿瘤亚体积的能力,以便对其进行额外的增敏照射。这项研究评估了FDG、FLT和Cu-ATSM PET成像预测犬类肿瘤放疗后残留FDG PET位置的能力。19只患有自发性鼻窦肿瘤的犬在进行低分割放疗前,使用放射性示踪剂FDG、FLT和Cu-ATSM进行了PET/CT成像。治疗方案为对鼻窦腔进行10次每次4.2 Gy的照射,对GTV有或无0.8 Gy的整合增敏照射。患者在第2次分割照射后进行了额外的FLT PET/CT扫描,在第3次分割照射后进行了Cu-ATSM PET/CT扫描,并在放疗后进行了随访FDG PET/CT扫描。在图像配准后,进行了简单和多元线性及逻辑体素回归分析,以评估治疗前和治疗中期PET成像对治疗后FDG摄取的预测效果。使用R(2)和伪R(2)评估拟合优度。对于简单线性回归模型,所有治疗前和治疗中期PET图像的回归系数在总体上均显著为正(P < 0.05)。然而,患者之间的拟合优度差异很大:R(2)范围为0.00至0.85,中位数为0.12。逻辑回归模型结果相似。多元线性回归模型拟合效果更好(中位数R(2) = 0.31),但患者之间的R(2)仍存在很大差异。不同预测变量回归模型的R(2)在患者之间高度相关(R ≈ 0.8),表明用一种示踪剂预测效果差的肿瘤,用其他示踪剂预测效果也差。总之,拟合优度的高患者间变异性表明PET能够在一些患者中预测残留肿瘤的位置,但在其他患者中则不能。这表明并非所有患者都适合基于单一生物学靶点进行剂量描绘。