Dynamic contrast-enhanced MRI of gastric cancer: Correlations of the pharmacokinetic parameters with histological type, Lauren classification, and angiogenesis

Purpose: To compare the pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in gastric cancers of different histological type and Lauren classification, and to investigate whether DCE-MRI parameters correlate with vascular endothelial growth factor (VEGF) expression levels in gastric cancer. Methods: Included were 32 patients with gastric cancer who underwent DCE-MRI of the upper abdomen before tumor resection. DCE-MRI parameters including the volume transfer coefficient (K-trans), reverse reflux rate constant (K-ep), and extracellular extravascular volume fraction (V-e) were calculated from the tumor region. Postoperative specimens were used for determination of histological differentiation (i.e., non-mucinous, mucinous, or signet-ring-cell adenocarcinoma) as well as Lauren classification (intestinal type or diffuse type). VEGF expression was examined for assessing angiogenesis. DCE-MRI parameters with different histological type and Lauren classification were compared using independent samples t-test and analysis of variance, respectively. Correlations between DCE-MRI parameters and VEGF expression grades were tested using Spearman correlation analysis. Results: Among gastric adenocarcinomas of three different histological types, mucinous adenocarcinomas showed a higher Ve and lower Ktrans than the others (P < 0.01). Between the two Lauren classifications, the diffuse type showed a higher V-e, than the intestinal type (P < 0.001). The mean K-trans showed a significantly positive correlation with VEGF (r = 0.762, P < 0.001). Conclusion: DCE-MRI permits noninvasive prediction of tumor histological type and Lauren classification and estimation of tumor angiogenesis in gastric cancer. DCE-MRI parameters can be used as imaging biomarkers to predict the biologic aggressiveness of a tumor as well as patient prognosis. (C) 2016 Elsevier Inc. All rights reserved.