Research Papers:

Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics

Bin Zhang, Fusheng Ouyang, Dongsheng Gu, Yuhao Dong, Lu Zhang, Xiaokai Mo, Wenhui Huang and Shuixing Zhang _

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Oncotarget. 2017; 8:72457-72465. https://doi.org/10.18632/oncotarget.19799

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Bin Zhang1,2,*, Fusheng Ouyang3,*, Dongsheng Gu4,*, Yuhao Dong5, Lu Zhang5, Xiaokai Mo5, Wenhui Huang5 and Shuixing Zhang1,2

1Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, P.R. China

2Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, P.R. China

3Department of Radiology, The First People’s Hospital of Shunde, Foshan, P.R. China

4Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, P.R. China

5Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, P.R. China

*Bin Zhang, Fusheng Ouyang, and Shengdong Gu contributed equally to this work

Correspondence to:

Shuixing Zhang, email: [email protected]

Keywords: imaging, biomarkers, nasopharyngeal carcinoma, progression, radiomics

Received: May 07, 2017     Accepted: June 28, 2017     Published: August 02, 2017


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.

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