Purpose

Elevated interstitial fluid pressure (IFP) in solid tumors can cause a substantial barrier to the fluid and macromolecules delivery [1].The pancreatic ductal adenocarcinoma (PDAC) microenvironment comprises stromal cells and an extracellular matrix [2]. The wick-in-needle technique is the gold-standard, but it is an invasive method of measuring tumors IFP [3]. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been explored as a potential method for the noninvasive measurement of IFP in preclinical and clinical settings [4-7]. Recently, computational fluid modeling (CFM) method [8] using DCE data has taken special attention for the estimation of IFP [9-12]. This study aims to use DCE MRI-based CFM for estimation of IFP in mouse models of PDAC.

Methods

Animals and Tumor Models: All procedures involving animals were approved by the Institutional Animal Care and Use Committee of Memorial Sloan Kettering Cancer Center. Tumors were established by injecting 2×105 KPC 4662 subcutaneously into the right shoulder region of athymic mice (n=4). The cells were originally derived from a murine pancreatic tumor, genotype Pdx1-Cre; LSL KRASG12D; Trp53R172H/wt. The MRI’s were performed 10-12 days after tumor inoculation.
MRI Data Acquisition: MRI was performed on a 9.4 T (Bruker BioSpin MRI GmbH). T2-weighted images with TR/ TE= 2092.34/33 ms, FOV=30 ×30 mm2, NA=2, NS=20, slice thickness= 0.7 mm, slice gap=0.7 mm, and acquisition matrix size (MS)=256×256 were used to locate the tumor slices for DCE-MRI slices. The DCE-MRI series were acquired using a FLASH sequence with TR/TE = 54.63/1.29 ms, NA=1, NS=6, flip angle (FA=15°), MS=132×106, and temporal resolution 5.79 s, resulting in a total acquisition time of 14 min 38 s. For precontrast T₁₀, T₁w images were acquired using four different TR values, i.e. 100 ms, 200 ms, 800 ms, and 2000 ms. Other scanning parameters were the same as mentioned above. DCE Data Analysis: Tumor regions of interest (ROI’s) were contoured using ITK-SNAP on dynamic images. For estimation of IFP, the volume transfer constant for CA, K^trans [min^-1], derived from the extended Tofts model (ETM) and arterial input function value (obtained from neck artery) were incorporated into the CFM simulation [9, 12].
Computational Theory for IFP: The CFM method is based on transport of CA in porous media providing estimates of IFP [9]. Starling equation of the vascular system consisting of source and sink terms [13] is incorporated into Equation [1] (Figure1), i.e., Darcy velocity, u , and IFP relationship [8 ]. The resulting equation modulated by K^trans is applied for CFM simulation (Equation [2]) is shown in Figure 1. CFM simulation was performed for Equation [2] using the following model parameters: vessel permeability L_p= 2 × 10−11 mPa⁻¹s⁻¹ (in tumor, t) and 3 × 10⁻¹² mPa⁻¹s⁻¹ (in normal tissue, n); lymphatic filtration coefficient L_pLS_L/V=1 × 10−7 Pa⁻¹ s⁻¹; tissue hydraulic conductivity K_H = 1.9 × 10⁻¹² (t), 3.8 × 10⁻¹³ (n) m² Pa⁻¹ s⁻¹; microvascular surface area-to-volume S/V= 2 × 104 (t), 7 × 10³ (n) m⁻¹; microvascular pressure p_v= 2300 Pa; osmotic pressure in microvasculature π_v= 2670 Pa; osmatic pressure in interstitial space π_i= 3230 (t), 1330 (n) Pa; and osmotic reflection coefficient for plasma σ= 0.82 (t), 0.91 (n) (Unitless).
CFM Simulation: Tumor ROI’s obtained were grayscale-dilated by 20 pixels, forming an extended domain ROI to incorporate normal tissue around tumor. ROI’s for tumor and extended domain were resliced 1mm3-isotropic, converted to STL format, and imported as boundary meshes for the model. The computations use finite element method in COMSOL Multiphysics software (COMSOL Inc., Stockholm, Sweden).

Results

We developed a noninvasive DCE method to estimate IFP for PDAC. ETM derived mean K^trans and CFM-estimated IFP values from four mice were 0.038±.004 (min ^-1) and 1.17±0.27 (kPa) respectively. A representative DCE data fitting with ETM is displayed in Figure 2. Representative T₂ weighted image, ETM derived K^trans map, and CFM generated IFP map are shown in Figure 3. Figure 4a shows a scatter plot of total tumor ROI volume vs. IFP. Tumor heterogeneity leading to the subtle differences in IFP values within the tumor are visualized in these maps. A weak positive linear relationship is observed between these metrics (r= 0.31). CFM-estimated IFP exhibited a strong linear relationship with K^trans (r= 0.90) (Figure 4b). The leakage space, v_e, and IFP exhibited weak positive correlation (r= 0.28).

Discussion

K^trans and IFP maps depict heterogeneity within the tumor. IFP is maximum at tumor core and monotonically decreases outwards to the tumor periphery towards the normal tissue. We observed a positive correlation between total tumor volume and IFP, and Ktrans and ve. Elevation of IFP in tumors are thought to be associated with the high vessel permeability and impaired lymphatic drainage from the interstitium. A linear relationship may be highly influenced by the tumor cellularity and stroma, depending on the tumor types [14].

Conclusion

After appropriate validation, non-invasive IFP from DCE MRI-based CFM estimate can have promising application in the clinics.

Acknowledgements

We acknowledge funding support from NCI R01 CA194321.

References

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