Zhang Bin, Ouyang Fusheng, Gu Dongsheng, Dong Yuhao, Zhang Lu, Mo Xiaokai, Huang Wenhui, Zhang Shuixing
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China.
Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China.
Oncotarget. 2017 Aug 2;8(42):72457-72465. doi: 10.18632/oncotarget.19799. eCollection 2017 Sep 22.
We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression. The selected features were used to construct radiomics-based models and the predictive performance of which were assessed with respect to the area under the curve (AUC). As a result, eight features significantly associated with the progression of advanced NPC were identified. In the training cohort, a radiomic model based on combined CET1-w and T2-w images (AUC: 0.886, 95%CI: 0.815-0.956) demonstrated better prognostic performance than models based on CET1-w (AUC: 0.793, 95%CI: 0.698-0.889) or T2-w images alone (AUC: 0.813, 95%CI: 0.721-0.904). These results were confirmed in the validation cohort. Accordingly, MRI-based radiomic biomarkers present high accuracy in the pre-treatment prediction of progression in advanced NPC.
我们旨在研究磁共振成像（MRI）的影像组学特征预测晚期鼻咽癌（NPC）患者病情进展的潜力。连续纳入113例晚期NPC患者（2007年1月至2013年7月）（训练队列：n = 80；验证队列：n = 33）。为每位患者从T2加权（T2-w）（n = 485）和对比增强T1加权（CET1-w）MRI（n = 485）中提取总共970个初始特征。我们使用最小绝对收缩和选择算子（Lasso）方法选择与病情进展最显著相关的特征。所选特征用于构建基于影像组学的模型，并根据曲线下面积（AUC）评估其预测性能。结果，确定了8个与晚期NPC病情进展显著相关的特征。在训练队列中，基于CET1-w和T2-w图像组合的影像组学模型（AUC：0.886，95%CI：0.815 - 0.956）显示出比基于CET1-w（AUC：0.793，95%CI：0.698 - 0.889）或单独T2-w图像（AUC：0.813，95%CI：0.721 - 0.904）的模型更好的预后性能。这些结果在验证队列中得到证实。因此，基于MRI的影像组学生物标志物在晚期NPC病情进展的治疗前预测中具有很高的准确性。
