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Updated: Jun 21, 2026
Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
Published on: April 18, 2015
Tumor characterization with dynamic contrast enhanced magnetic resonance imaging and biodegradable macromolecular
Xueming Wu1, Yi Feng, Eun-Kee Jeong
1Department of Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT, USA.
Purpose:
To investigate the efficacy of polydisulfide-based biodegradable macromolecular contrast agents of different degradability and molecular weight for tumor characterization based on angiogenesis using dynamic contrast enhanced MRI (DCE-MRI).
Methods:
Biodegradable macromolecular MRI contrast agents, Gd-DTPA cystamine copolymers (GDCC) and Gd-DTPA cystine copolymers (GDCP), with molecular weight of 20 and 70 KDa were evaluated for tumor characterization. Gd(DTPA-BMA) and a prototype of macromolecular contrast agent, albumin-(Gd-DTPA), were used as controls. The DCE-MRI studies were performed in nude mice bearing MDA PCa 2b and PC-3 human prostate tumor xenografts. Tumor angiogenic kinetic parameters including endothelium transfer coefficient (K(trans)) and fractional tumor plasma volume (f(PV)) were calculated from the DCE-MRI data using a two-compartment model and compared between the two different tumor models for each contrast agent.
Results:
There was no significant difference in the f(PV) values between two tumor models estimated with the same agent except for GDCC-70. The K(trans) values in both tumor models decreased with the increase of molecular weight of contrast agents. With the same high molecular weight (70 KDa), GDCC-70 showed a higher K(trans) values than GDCP-70 due to high degradability of the former in both tumor models (p < 0.05). The K(trans) values of MDA PCa 2b tumors were significantly higher than those of PC-3 tumors estimated by Gd(DTPA-BMA), GDCC-20, GDCC-70, GDCP-70, and albumin-(Gd-DTPA) (p < 0.05).
Conclusions:
The polydisulfide-based biodegradable macromolecular MRI contrast agents are promising in tumor characterization and differentiation with dynamic contrast enhanced MRI.
