In vivo measurement of gadolinium diffusivity by dynamic contrast‐enhanced MRI: A preclinical study of human xenografts

Compartmental tracer kinetic models currently used for analysis of dynamic contrast‐enhanced MRI data yield poor fittings or parameter values that are unphysiological in necrotic regions of the tumor, as these models only describe microcirculation in perfused tissue. In this study, we explore the use of Fick's law of diffusion as an alternative method for analysis of dynamic contrast‐enhanced MRI data in the necrotic regions. Xenografts of various human cancer cell lines were implanted in 14 mice that were subjected to dynamic contrast‐enhanced MRI performed using a spoiled gradient recalled sequence. Tracer concentration was estimated using the variable flip angle technique. Poorly perfused and necrotic tumor regions exhibiting delayed and slow enhancement were identified using a k‐means clustering algorithm. Tracer behavior in necrotic regions was shown to be consistent with Fick's diffusion equation and the in vivo gadolinium diffusivity was estimated to be 2.08 (±0.88) × 10−4 mm2/s. This study proposes the use of gadolinium diffusivity as an alternative parameter for quantifying tracer transport within necrotic tumor regions. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

[1]  R K Jain,et al.  Transport of molecules in the tumor interstitium: a review. , 1987, Cancer research.

[2]  Edward V R Dibella,et al.  Model‐based blind estimation of kinetic parameters in dynamic contrast enhanced (DCE)‐MRI , 2009, Magnetic resonance in medicine.

[3]  T. Peters,et al.  High‐resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2 , 2005, Magnetic resonance in medicine.

[4]  D. Collins,et al.  Diffusion-weighted MRI in the body: applications and challenges in oncology. , 2007, AJR. American journal of roentgenology.

[5]  M Intaglietta,et al.  Tissue perfusion inhomogeneity during early tumor growth in rats. , 1979, Journal of the National Cancer Institute.

[6]  C. Thng,et al.  Dynamic contrast‐enhanced MRI of neuroendocrine hepatic metastases: A feasibility study using a dual‐input two‐compartment model , 2011, Magnetic resonance in medicine.

[7]  Gregory S Karczmar,et al.  Multiple reference tissue method for contrast agent arterial input function estimation , 2007, Magnetic resonance in medicine.

[8]  E. Rofstad,et al.  Dynamic contrast‐enhanced magnetic resonance imaging of human melanoma xenografts with necrotic regions , 2007, Journal of magnetic resonance imaging : JMRI.

[9]  R. Jain,et al.  Measurement of macromolecular diffusion coefficients in human tumors. , 2004, Microvascular research.

[10]  G. Brix,et al.  Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI , 2010, European Radiology.

[11]  E. Rofstad,et al.  Dynamic contrast‐enhanced‐MRI of tumor hypoxia , 2012, Magnetic resonance in medicine.

[12]  H. Huynh,et al.  Xenografts of Human Hepatocellular Carcinoma: A Useful Model for Testing Drugs , 2006, Clinical Cancer Research.

[13]  Yuan-Cheng Fung,et al.  Introduction to Bioengineering , 2001 .

[14]  C. R. Ethier,et al.  Measurement of Gd-DTPA diffusion through PVA hydrogel using a novel magnetic resonance imaging method. , 1999, Biotechnology and bioengineering.

[15]  Paolo A. Netti,et al.  Interstitial Transport in Solid Tumours , 2003 .