Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis

Abstract

We developed and tested the feasibility of computational fluid modeling (CFM) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantitative estimation of interstitial fluid pressure (IFP) and velocity (IFV) in patients with head and neck (HN) cancer with locoregional lymph node metastases. Twenty-two patients with HN cancer, with 38 lymph nodes, underwent pretreatment standard MRI, including DCE-MRI, on a 3-Tesla scanner. CFM simulation was performed with the finite element method in COMSOL Multiphysics software. The model consisted of a partial differential equation (PDE) module to generate 3D parametric IFP and IFV maps, using the Darcy equation and Ktrans values (min−1, estimated from the extended Tofts model) to reflect fluid influx into tissue from the capillary microvasculature. The Spearman correlation (ρ) was calculated between total tumor volumes and CFM estimates of mean tumor IFP and IFV. CFM-estimated tumor IFP and IFV mean ± standard deviation for the neck nodal metastases were 1.73 ± 0.39 (kPa) and 1.82 ± 0.9 × (10−7 m/s), respectively. High IFP estimates corresponds to very low IFV throughout the tumor core, but IFV rises rapidly near the tumor boundary where the drop in IFP is precipitous. A significant correlation was found between pretreatment total tumor volume and CFM estimates of mean tumor IFP (ρ = 0.50, P = 0.004). Future studies can validate these initial findings in larger patients with HN cancer cohorts using CFM of the tumor in concert with DCE characterization, which holds promise in radiation oncology and drug-therapy clinical trials.

Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure

“…The pre-contrast T 1w FAs data were fitted for T 10 mapping as detailed elsewhere [ 39 ]. The R 1 data with time were fitted to Equation [ 6 ] using the AIF was extracted from the carotid artery [ 25 ].…”

Longitudinal Monitoring of Simulated Interstitial Fluid Pressure for Pancreatic Ductal Adenocarcinoma Patients Treated with Stereotactic Body Radiotherapy

“…CA enters passively into the EES via diffusion from the blood plasma in a hyperpermeable capillary, which is described by K trans . To account for the bulk flow of spatially varying CA into the EES within the tumor [43][44][45]49], the normalized K trans value is incorporated into the continuity equation (Equation ( 8)), including the Starling equation of net flux change between vascular and lymphatic vessels. Note, a voxel-wise K trans value was normalized and included in the equation as a factor to convey relative vascular leakiness.…”

“…To assess the robustness of the approach, IFP computed from the DCE-MRI approach was compared with the standard WIN method [39,41]. The results of previous studies suggested that the microvascular and interstitial hydraulic conductivities influence the outcome of the porous media fluid flow simulation [41][42][43][44][45][46]. However, the correlations need further validation to be part of clinical practice.…”

“…The 2nd order PD equation incorporates the Starling equation [24] and Darcy velocity-pressure relationship [47]. The CFM simulation utilizes the tumor tissue physiological parameters measured in pre-and clinical settings such as capillary and tissue hydraulic conductivities, including K trans values as input parameters for simulation of IFP and IFV [45,46,48,49].To the best of our knowledge, the DCE-based CFM approach for estimating quantitative IFP and IFV values in patients with PDAC is still unexplored. Herein, DCE data acquired at three-time points (i.e., pretreatment (TX), immediately after the first fraction of SBRT, and post-TX) in the previous study were used [22].…”

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

“…Although the spatial scales of in vivo assessments of tumor physiology are typically limited to 0.25-5 mm (Frangioni, 2008;Ramamonjisoa and Ackerstaff, 2017), these techniques do enable measurement of temporal dynamics within the tumor microenvironment (Yankeelov et al, 2016b), such as vascular perfusion (Yankeelov et al, 2014;Yankeelov and Gore, 2009), metabolic activity (Castell and Cook, 2008), or interstitial flow (Kingsmore et al, 2018;LoCastro et al, 2020). These data types can also be acquired in 3D to evaluate an entire tumor volume, permitting investigation of regional differences within a tumor (e.g., regions of necrosis or proliferation, gradients of oxygen or nutrients) (Kim and Gatenby, 2017;Syed et al, 2020).…”