Yue Yong, Osipov Arsen, Fraass Benedick, Sandler Howard, Zhang Xiao, Nissen Nicholas, Hendifar Andrew, Tuli Richard
Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
J Gastrointest Oncol. 2017 Feb;8(1):127-138. doi: 10.21037/jgo.2016.12.04.
To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients.
Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis.
Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%).
Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.
利用放疗前后的PET/CT图像对胰腺腺癌（PA）患者的风险进行分层，并评估纹理变化在预测患者治疗反应中的预后价值。
确定了2011年至2013年接受放疗且治疗前后均进行18F-FDG-PET/CT扫描的26例PA患者。使用基于3D核的方法计算肿瘤局部纹理，并通过拟合治疗前后图像纹理图的差异来识别纹理变化。共识别并评估了48个纹理和临床变量与总生存期（OS）的相关性。使用套索/弹性网络回归选择预后异质性特征，并通过多变量Cox分析进一步评估。
中位年龄为69岁（范围46-86岁）。治疗前后的纹理图和时间变化通过直方图和统计拟合得到了很好的表征。套索分析确定了七个预测因子（年龄、淋巴结分期、放疗后SUVmax、均匀性变化、方差、总和均值以及聚类倾向）。多变量Cox分析确定了五个显著变量：年龄、淋巴结分期、均匀性变化、方差和聚类倾向（P值分别为0.020、0.040、0.065、0.078和0.081）。根据多变量分析的风险评分将患者分为两组，对数秩检验P=0.001：低风险组（n=11），平均OS较长（29.3个月）且纹理变化较高（>30%）；高风险组（n=15），平均OS较短（17.7个月）且纹理变化较低（<15%）。
局部代谢纹理反应为评估和预测PA放疗后的临床结果提供了一种可行的方法。所提出的方法可用于对患者风险进行分层，并有助于为个体患者选择合适的治疗策略，以实施反应驱动的自适应放疗。
