Noninvasive Estimation of Tumor Interstitial Fluid Pressure by DCE-MRI and Its Confirmation by Invasive Wick-In-Needle Technique in a Rat Glioblastoma Model
Nagaraja TN, Elmghirbi R, Brown SL, Panda S, Cabral G, Lee IY, Knight RA, and Ewing JR. Noninvasive Estimation of Tumor Interstitial Fluid Pressure by DCE-MRI and Its Confirmation by Invasive Wick-In-Needle Technique in a Rat Glioblastoma Model. Fluids Barriers CNS 2019; 16.
Fluids Barriers CNS
Objective: Dysfunctional blood-tumor barrier and elevated tumor interstitial fluid pressure (TIFP) limit perfusion and increase hypoxia. TIFP contributes to peri-tumoral exudate flow and edema. Peri-tumoral exudate flow and edema counter tumor penetration by chemotherapeutics. Increased TIFP is also a mark of tumor aggressiveness, and decreased TIFP, a predictor of response to therapy. However, non-invasive techniques are unavailable for measuring TIFP in cerebral tumors. This study employed dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to estimate TIFP and its confirmation by an invasive method using a rat glioblastoma model. Methods: Athymic rats (n = 22) were implanted intracerebrally with U251 glioblastoma. At 2 weeks post-implantation, DCE-MRI was conducted in control (n = 13) and bevacizumab-treated rats (n = 9), followed by invasive TIFP measurement in each animal using a 'wick-in-needle' (WIN) method. Applying model selection paradigm, Patlak- and Loganplots to DCE-MRI data, extracellular volume fraction (porosity) and velocity of exudate fluid flow at the tumor boundary were derived to estimate TIFP by Darcy's law. Two models, a fluid-mechanical model and a multivariate empirical model, were used for TIFP estimations and verified against WIN-TIFP. WIN-TIFP and MRI-TIFP data were tested for correlation by linear regression and significance inferred at p < 0.05. Results: Using DCE-MRI, the mean estimated hydraulic conductivity (MRI-K) was (2.3 ± 3.1) x 10-5 ( mm2/mm Hg-s) in control studies. Significant positive correlations were found between WIN-TIFP and MRI-TIFP in both mechanical and empirical models. For instance, in the control group of the fluid-mechanical model, MRI-TIFP was a strong predictor of WIN-TIFP (R = 0.76; p < .01). Similar result was found in the bevacizumabtreated group in the empirical model (R = 0.87; p < .01). In controls mean WIN-TIFP was 6.0 ± 3.7 and MRI-TIFP, 6.2 ± 3.7. Bevacizumab decreased the mean TIFP, albeit to slightly varying degrees by the two methods, 2.8 ± 1.6 (WIN) and 5.3 ± 3.3 (MRI). Both control and bevacizumab groups showed a high degree of inter-method correlation with R = 0.9 (p < 0.01) between the WIN- and MRI-TIFP measurements. Conclusion: These data suggest that DCE-MRI studies contain enough information to noninvasively estimate TIFP in this, and possibly other, glioma models and, thus, might be useful to assess tumor aggressiveness and responses to therapies aiming to decrease TIFP and increase tumor drug delivery.