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Intratumor distribution and test–retest comparisons of physiological parameters quantified by dynamic contrast‐enhanced MRI in rat U251 glioma
Author(s) -
Aryal Madhava P.,
Nagaraja Tavarekere N.,
Brown Stephen L.,
Lu Mei,
BagherEbadian Hassan,
Ding Guangliang,
Panda Swayamprava,
Keenan Kelly,
Cabral Glauber,
Mikkelsen Tom,
Ewing James R.
Publication year - 2014
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.3178
Subject(s) - glioma , contrast (vision) , dynamic contrast , dynamic contrast enhanced mri , distribution (mathematics) , magnetic resonance imaging , nuclear magnetic resonance , pathology , biomedical engineering , biology , medicine , computer science , radiology , cancer research , mathematics , artificial intelligence , physics , mathematical analysis
The distribution of dynamic contrast‐enhanced MRI (DCE‐MRI) parametric estimates in a rat U251 glioma model was analyzed. Using Magnevist as contrast agent (CA), 17 nude rats implanted with U251 cerebral glioma were studied by DCE‐MRI twice in a 24 h interval. A data‐driven analysis selected one of three models to estimate either (1) plasma volume ( v p ), (2) v p and forward volume transfer constant ( K trans ) or (3) v p , K trans and interstitial volume fraction ( v e ), constituting Models 1, 2 and 3, respectively. CA distribution volume ( V D ) was estimated in Model 3 regions by Logan plots. Regions of interest (ROIs) were selected by model. In the Model 3 ROI, descriptors of parameter distributions – mean, median, variance and skewness – were calculated and compared between the two time points for repeatability. All distributions of parametric estimates in Model 3 ROIs were positively skewed. Test–retest differences between population summaries for any parameter were not significant ( p ≥ 0.10; Wilcoxon signed‐rank and paired t tests). These and similar measures of parametric distribution and test–retest variance from other tumor models can be used to inform the choice of biomarkers that best summarize tumor status and treatment effects. Copyright © 2014 John Wiley & Sons, Ltd.