Research Papers:

Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma

Zhen Hou, Wei Ren, Shuangshuang Li, Juan Liu, Yu Sun, Suiren Wan and Suiren Wan _

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Oncotarget. 2017; 8:104444-104454. https://doi.org/10.18632/oncotarget.22304

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Zhen Hou1,*, Wei Ren2,*, Shuangshuang Li2, Juan Liu2, Yu Sun1, Jing Yan2 and Suiren Wan1

1State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China

2The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing, Jiangsu 210008, China

*These authors have contributed equally to this work

Correspondence to:

Suiren Wan, email: [email protected]

Jing Yan, email: [email protected]

Keywords: esophageal carcinoma; computed tomography; radiomics analysis; predictor; treatment response

Received: August 16, 2017    Accepted: October 05, 2017    Published: November 06, 2017


Objectives: To investigate the capability of computed-tomography (CT) radiomic features to predict the therapeutic response of Esophageal Carcinoma (EC) to chemoradiotherapy (CRT).

Methods: Pretreatment contrast-enhanced CT images of 49 EC patients (33 responders, 16 nonresponders) who received with CRT were retrospectively analyzed. The region of tumor was contoured by two radiologists. A total of 214 features were extracted from the tumor region. Kruskal-Wallis test and receiver operating characteristic (ROC) analysis were performed to evaluate the capability of each feature on treatment response classification. Support vector machine (SVM) and artificial neural network (ANN) algorithms were used to build models for prediction of the treatment response. The statistical difference between the performances of the models was assessed using McNemar’s test.

Results: Radiomic-based classification showed significance in differentiating responders from nonresponders. Five features were found to discriminate nonresponders from responders (AUCs from 0.686 to 0.727). Considering these features, two features (Histogram2D_skewness: P = 0.015. Histogram2D_kurtosis: P = 0.039) were significant for differentiating SDs (stable disease) from PRs (partial response) and one feature (Histogram2D_skewness: P = 0.027) for differentiating SDs from CRs (complete response). Both classifiers showed potential in predicting the treatment response with higher accuracy (ANN: 0.972, SVM: 0.891). No statistically significant difference was observed in the performance of the two classifiers (P = 0.250).

Conclusions: CT-based radiomic features can be used as imaging biomarkers to predict tumor response to CRT in EC patients.

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