Peritumoral tissue compression is predictive of exudate flux in a rat model of cerebral tumor: an MRI study in an embedded tumor

Abstract

MRI estimates of extracellular volume and tumor exudate flux in peritumoral tissue are demonstrated in an experimental model of cerebral tumor. Peritumoral extracellular volume predicted the tumor exudate flux. Eighteen RNU athymic rats were inoculated intracerebrally with U251MG tumor cells and studied with dynamic contrast-enhanced MRI (DCE-MRI) approximately 18 days post-implantation. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the distribution volume (i.e., tissue porosity) in the leaky rim of the tumor, and in the tissue external to the rim (the outer rim), was estimated, as was the tumor exudate flow from the inner rim of the tumor through the outer rim. Distribution volume in the outer rim was approximately half that of the inner adjacent region (p < 1×10−4). The distribution volume of the outer ring was significantly correlated (R2 = 0.9) with tumor exudate flow from the inner rim. Thus, peritumoral extracellular volume predicted the rate of tumor exudate flux. One explanation for these data is that perfusion, i.e., the delivery of blood to the tumor, was regulated by the compression of the mostly normal tissue of the tumor rim, and that the tumor exudate flow was limited by tumor perfusion.

Toward a noninvasive estimate of interstitial fluid pressure by dynamic contrast‐enhanced MRI in a rat model of cerebral tumor

“…A simple calculation for the area of the perimeter of the tumor yields flux from the tumor. See Figures 8 and 9 of Ref , which show the very highly correlated relationship (R 2 = 0.9) between normal tissue compression and exudate flux.…”

“…As in Ref , MRI vascular parameters were estimated in 3 ROIs with differing physiologies: the whole tumor ROI, a one‐pixel‐wide ROI defining the leaky rim of the tumor, and a one‐pixel ROI in the adjacent normal tissue immediately surrounding the tumor. The MRI vascular and volume parameters used in estimating TIFP and associated with TIFP in the multivariate analysis were as follows: (1) arterial spin labeling for estimating tumor blood flow (TBF) in the tumor itself, in the leaky rim of the tumor (TBF ‐rim ), and in the adjacent normal tissue surrounding the tumor (TBF ‐peri ); (2) extended Patlak analysis of DCE‐MRI data to estimate the forward volume transfer constant (K trans ) in the tumor and in the tumor rim ( ); (3) Logan analysis of DCE‐MRI data to estimate the extracellular volume fraction (V D ) in the tumor, extracellular volume fraction (V D‐rim ) in the leaky rim of the tumor, and extracellular volume fraction (V D‐peri ) in the mostly normal tissue immediately adjacent to the tumor (see subsequently); and (4) a graphical method to estimate the flux of exudate fluid across the boundary of the tumor (see subsequently). One direct benefit of model selection in and around the tumor is that an unambiguous delineation of the tumor extent is formed by the boundary of the Model 3 and Model 2 regions (Figure ).…”

“…Ref addresses the estimation of extracellular space in the mostly normal rim. It is assumed that the CA is not re‐absorbed in the microvasculature of the mostly normal rim, and that at some time point in the experiment the concentration of CA leaving the normal rim equilibrates with the concentration of CA entering the normal rim.…”

“…This assumption can be verified (and the time of equilibration identified) by use of a Logan plot, with the concentration of the inner adjacent tumor voxels used as the input function to the outer normal voxels. See Figure 7 of Ref for an example of a curve fit that establishes an estimate of extracellular space in the normal rim of a tumor.…”

“…After determining the value of S in the ROI of the tumor, and using the previously acquired experimental estimates of porosity, the hydraulic conductivity (MRI‐K) was derived using the KC relationship. Using the methods of reference, the mean velocity of exudate fluid ( ) across the surface of the tumor was estimated. Using the pressure‐depth profile, the length of the pressure gradient was calculated.…”