DCE-MRI perfusion and permeability parameters as predictors of tumor response to CCRT in patients with locally advanced NSCLC

Tao, Xiuli and Wang, Lvhua and Hui, Zhouguang and Liu, Li and Ye, Feng and Song, Ying and Tang, Yu and Men, Yu and Lambrou, Tryphon and Su, Zihua and Xu, Xiao and Ouyang, Han and Wu, Ning (2016) DCE-MRI perfusion and permeability parameters as predictors of tumor response to CCRT in patients with locally advanced NSCLC. Scientific Reports, 6 . p. 35569. ISSN 2045-2322

Full content URL: http://www.nature.com/articles/srep35569

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Abstract

In this prospective study, 36 patients with stage III non-small cell lung cancers (NSCLC), who underwent dynamic contrast-enhanced MRI (DCE-MRI) before concurrent chemo-radiotherapy (CCRT) were enrolled. Pharmacokinetic analysis was carried out after non-rigid motion registration. The perfusion parameters including Blood Flow (BF), Blood Volume (BV), Mean Transit Time (MTT) and permeability parameters including endothelial transfer constant (Ktrans), reflux rate (Kep), fractional extravascular extracellular space volume (Ve), fractional plasma volume (Vp) were calculated, and their relationship with tumor regression was evaluated. The value of these parameters on predicting responders were calculated by receiver operating characteristic (ROC) curve. Multivariate logistic regression analysis was conducted to find the independent variables. Tumor regression rate is negatively correlated with V e and its standard variation V e-SD and positively correlated with K trans and Kep. Significant differences between responders and non-responders existed in Ktrans, Kep, Ve, Ve-SD, MTT, BV-SD and MTT-SD (P < 0.05). ROC indicated that Ve < 0.24 gave the largest area under curve of 0.865 to predict responders. Multivariate logistic regression analysis also showed Ve was a significant predictor. Baseline perfusion and permeability parameters calculated from DCE-MRI were seen to be a viable tool for predicting the early treatment response after CCRT of NSCLC. © 2016 The Author(s).

Keywords:Cancer imaging, Non-small-cell lung cancer, JCOpen
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:24913
Deposited On:04 Nov 2016 11:33

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