Analysis of mice tumor models using dynamic MRI data and a dedicated software platform*.

PURPOSE: To implement a software platform (DynaVision) dedicated to analyze data from functional imaging of tumors with different mathematical approaches, and to test the software platform in pancreatic carcinoma xenografts in mice with severe combined immunodeficiency disease (SCID).

MATERIALS AND METHODS: A software program was developed for extraction and visualization of tissue perfusion parameters from dynamic contrast-enhanced images. This includes regional parameter calculation from enhancement curves, parametric images (e. g., blood flow), animation, 3D visualization, two-compartment modeling, a mode for comparing different datasets (e. g., therapy monitoring), and motion correction. We analyzed xenograft tumors from two pancreatic carcinoma cell lines (BxPC3 and ASPC1) implanted in 14 SCID mice after injection of Gd-DTPA into the tail vein. These data were correlated with histopathological findings.

RESULTS: Image analysis was completed in approximately 15 minutes per data set. The possibility of drawing and editing ROIs within the whole data set makes it easy to obtain quantitative data from the intensity-time curves. In one animal, motion artifacts reduced the image quality to a greater extent but data analysis was still possible after motion correction. Dynamic MRI of mice tumor models revealed a highly heterogeneous distribution of the contrast-enhancement curves and derived parameters, which correlated with differences in histopathology. ASPC1 tumors showed a more hypervascular type of curves with faster and higher signal enhancement rate (wash-in) and a faster signal decrease (wash-out). BXPC3 tumors showed a more hypovascular type with slower wash-in and wash-out. This correlated with the biological properties of the tumors.

CONCLUSION: With the described software, it was possible to analyze tissue perfusion parameters in small xenograft tumor models in mice. Our data correlated with histopathological data, and the qualitative and quantitative perfusion parameters could distinguish two tumor entities with different growth characteristics.