Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma

被引:32
|
作者
Hou, Zhen [1 ]
Li, Shuangshuang [2 ,3 ]
Ren, Wei [2 ,3 ]
Liu, Juan [2 ,3 ]
Yan, Jing [2 ,3 ]
Wan, Suiren [1 ]
机构
[1] Southeast Univ, Sch Biol Sci & Med Engn, State Key Lab Bioelect, Lab Med Elect, Nanjing 210096, Jiangsu, Peoples R China
[2] Nanjing Univ, Comprehens Canc Ctr, Drum Tower Hosp, Med Sch, Nanjing 210000, Jiangsu, Peoples R China
[3] Nanjing Univ, Clin Canc Inst, Nanjing 210000, Jiangsu, Peoples R China
关键词
Esophageal carcinoma (EC); magnetic resonance imaging (MRI); radiomics analysis; predictor; treatment response; CONTRAST-ENHANCED MRI; TEXTURE ANALYSIS; NEOADJUVANT CHEMORADIOTHERAPY; PATHOLOGICAL RESPONSE; PROGNOSTIC VALUE; CANCER-PATIENTS; CT TEXTURE; FEATURES; HETEROGENEITY; CHEMOTHERAPY;
D O I
10.21037/jtd.2018.03.123
中图分类号
R56 [呼吸系及胸部疾病];
学科分类号
摘要
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.
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收藏
页码:2256 / +
页数:13
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