Computational modeling of interstitial fluid pressure and velocity in head and neck cancer based on dynamic contrast-enhanced magnetic resonance imaging: Feasibility analysis Journal Article


Authors: LoCastro, E.; Paudyal, R.; Mazaheri, Y.; Hatzoglou, V.; Oh, J. H.; Lu, Y.; Konar, A. S.; vom Eigen, K.; Ho, A.; Ewing, J. R.; Lee, N.; Deasy, J. O.; Shukla-Dave, A.
Article Title: 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 formula presented 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. © 2020 The Authors. Published by Grapho Publications, LLC.
Keywords: head and neck cancer; dynamic contrast-enhanced mri; lymph node metastases; computational fluid modeling; darcy velocity; extended tofts model; interstitial fluid pressure and velocity
Journal Title: Tomography
Volume: 6
Issue: 2
ISSN: 2379-1381
Publisher: MDPI  
Date Published: 2020-06-01
Start Page: 129
End Page: 138
Language: English
DOI: 10.18383/j.tom.2020.00005
PUBMED: 32548289
PROVIDER: scopus
PMCID: PMC7289251
DOI/URL:
Notes: Article -- Source: Scopus
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  1. Nancy Y. Lee
    871 Lee
  2. Amita Dave
    137 Dave
  3. Jung Hun Oh
    187 Oh
  4. Alan Loh Ho
    237 Ho
  5. Joseph Owen Deasy
    524 Deasy
  6. Ramesh Paudyal
    38 Paudyal