T2加权和短TI反转恢复序列T2加权磁共振成像中的影像组学分析：预测食管鳞状细胞癌对放化疗的治疗反应
Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma.
作者信息
Hou Zhen, Li Shuangshuang, Ren Wei, Liu Juan, Yan Jing, Wan Suiren
机构信息
State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China.
The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China.
出版信息
J Thorac Dis. 2018 Apr;10(4):2256-2267. doi: 10.21037/jtd.2018.03.123.
BACKGROUND
To investigate the capability of radiomic analysis using T2-weighted (T2W) and spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI) for predicting the therapeutic response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT).
METHODS
Pretreatment T2W- and SPAIR T2W-MRI of 68 ESCC patients (37 responders, 31 nonresponders) were analyzed. A number of 138 radiomic features were extracted from each image sequence respectively. Kruskal-Wallis test were performed to evaluate the capability of each feature on treatment response classification. Sensitivity and specificity for each of the studied features were derived using receiver operating characteristic (ROC) analysis. Support vector machine (SVM) and artificial neural network (ANN) models were constructed based on the training set (23 responders, 20 nonresponders) for the prediction of treatment response, and then the testing set (14 responders, 11 nonresponders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test.
RESULTS
Radiomic analysis showed significance in the prediction of treatment response. The analyses showed that complete responses (CRs) versus stable diseases (SDs), partial responses (PRs) versus SDs, and responders (CRs and PRs) versus nonresponders (SDs) could be differentiated by 26, 17, and 33 features (T2W: 11/11/15, SPAIR T2W: 15/6/18), respectively. The prediction models (ANN and SVM) based on features extracted from SPAIR T2W sequence (SVM: 0.929, ANN: 0.883) showed higher accuracy than those derived from T2W (SVM: 0.893, ANN: 0.861). No statistical difference was observed in the performance of the two classifiers (P=0.999).
CONCLUSIONS
Radiomic analysis based on pretreatment T2W- and SPAIR T2W-MRI can be served as imaging biomarkers to predict treatment response to CRT in ESCC patients.
背景
探讨利用T2加权（T2W）和频谱衰减反转恢复T2加权（SPAIR T2W）磁共振成像（MRI）进行影像组学分析，以预测食管鳞状细胞癌（ESCC）对放化疗（CRT）治疗反应的能力。
方法
分析68例ESCC患者（37例反应者，31例无反应者）治疗前的T2W和SPAIR T2W-MRI图像。分别从每个图像序列中提取138个影像组学特征。采用Kruskal-Wallis检验评估每个特征对治疗反应分类的能力。利用受试者工作特征（ROC）分析得出每个研究特征的敏感性和特异性。基于训练集（23例反应者，20例无反应者）构建支持向量机（SVM）和人工神经网络（ANN）模型，用于预测治疗反应，然后用测试集（14例反应者，11例无反应者）验证模型的可靠性。采用McNemar检验对模型性能进行比较。
结果
影像组学分析在预测治疗反应方面具有重要意义。分析表明，完全缓解（CR）与疾病稳定（SD）、部分缓解（PR）与SD、反应者（CR和PR）与无反应者（SD）分别可通过26、17和33个特征（T2W：11/11/15，SPAIR T2W：15/6/18）进行区分。基于从SPAIR T2W序列中提取的特征构建的预测模型（ANN和SVM）（SVM：0.929，ANN：0.883）显示出比基于T2W构建的模型（SVM：0.893，ANN：0.861）更高的准确性。两个分类器的性能未观察到统计学差异（P=0.999）。
结论
基于治疗前T2W和SPAIR T2W-MRI的影像组学分析可作为预测ESCC患者对CRT治疗反应的影像生物标志物。
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