Related Experiment Video
Updated: Mar 25, 2026

Development and Angiographic Use of the Rabbit VX2 Model for Liver Cancer
Published on: January 7, 2019
Diffusion-weighted multiparametric MRI for monitoring longitudinal changes of parameters in rabbit VX2 liver tumors
Haijun Wu1,2, Hui Liu1, Changhong Liang1
1Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China.
Purpose:
To investigate the value of different quantitative models of diffusion-weighted multiparametric imaging (DW-MPI) including traditional as well as several advanced models for monitoring the longitudinal parameter changes in rabbit liver VX2 carcinoma and for correlating the perfusion-related imaging parameters to vascularity of tumor tissue.
Materials And Methods:
Rabbit liver VX2 carcinoma was imaged by DW-MPI at the 2nd, 3rd, and 4th weeks after tumor implantation at 1.5T using two sets of b values ranging from 0 to 2000 s/mm(2) . Serial parameter changes of each model at three timepoints were compared. Univariate and multivariate regression analyses were carried out to analyze the ability of perfusion-related parameters, including apparent diffusion coefficient (ADC), perfusion fraction (f), and pseudodiffusion coefficient (D*), to predict mean microvessel density (MVD) as determined by quantitative histopathology.
Results:
For the period from Week 2 to Week 4, the measurements of ADC, f, and KDKI illustrated a statistical difference (P = 0.000, P = 0.000, and P = 0.002, respectively), whereas the comparison of D, D*, DDKI , DSEM , and αSEM demonstrated no statistical significance. ADC and f showed highest correlation with MVD at Week 4 (r(2) = 0.307, P = 0.017, and r(2) = 0.402, P < 0.01, respectively). Multivariate analyses confirmed highest correlation of f and ADC with MVD at Week 4 (P = 0.001 and P = 0.002).
Conclusion:
ADC, f, and KDKI were identified as the most promising parameters for monitoring changes in rabbit liver VX2 carcinoma and f and ADC showed highest correlation with MVD. J. Magn. Reson. Imaging 2016;44:707-714.

