DCE-MRI Perfusion and Permeability Parameters as predictors of tumor response to CCRT in Patients with locally advanced NSCLC

X. Tao, L. Wang, Z. Hui, L. Liu, F. Ye, Y. Song, Y. Tang, Y. Men, T. Lambrou, Z. Su, X. Xu, H. Ouyang, N. Wu

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27 Citations (Scopus)


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 Ve and its standard variation Ve_SD and positively correlated with Ktrans 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
Original languageEnglish
Article number35569
JournalScientific Reports
Publication statusPublished - 20 Oct 2016

Bibliographical note

We would like to express our gratitude to the technical support and assistance from Dr. Huang, Ning from Life Science, GE Healthcare China. This work was funded by the National High Technology Research and Development Program of China (863 Program, Grant No. 2014AA020602) and The Innovation Funds of Peking Union Medical College (Grant No. 2013-1002-20).

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