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Tomography
Volume 6
Issue 2
10.18383/j.tom.2020.00005
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Article

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

by
Eve LoCastro
1,
Ramesh Paudyal
1,
Yousef Mazaheri
1,2,
Vaios Hatzoglou
2,
Jung Hun Oh
1,
Yonggang Lu
3,
Amaresha Shridhar Konar
1,
Kira vom Eigen
1,
Alan Ho
4,
James R. Ewing
5,6,
Nancy Lee
7,
Joseph O. Deasy
1 and
Amita Shukla-Dave
1,2,*
1
Departments of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
2
Departments of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
3
Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
4
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
5
Departments of Neurology, Detroit, MI, USA
6
Departments of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
7
Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(2), 129-138; https://doi.org/10.18383/j.tom.2020.00005
Submission received: 5 March 2020 / Revised: 8 April 2020 / Accepted: 14 May 2020 / Published: 1 June 2020
Versions Notes

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.
Keywords: computational fluid modeling; lymph node metastases; interstitial fluid pressure and velocity; head and neck cancer; dynamic contrast-enhanced MRI; extended Tofts model; Darcy velocity computational fluid modeling; lymph node metastases; interstitial fluid pressure and velocity; head and neck cancer; dynamic contrast-enhanced MRI; extended Tofts model; Darcy velocity

Share and Cite

MDPI and ACS Style

LoCastro, E.; Paudyal, R.; Mazaheri, Y.; Hatzoglou, V.; Oh, J.H.; Lu, Y.; Konar, A.S.; Eigen, K.v.; Ho, A.; Ewing, J.R.; et al. Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis. Tomography 2020, 6, 129-138. https://doi.org/10.18383/j.tom.2020.00005

AMA Style

LoCastro E, Paudyal R, Mazaheri Y, Hatzoglou V, Oh JH, Lu Y, Konar AS, Eigen Kv, Ho A, Ewing JR, et al. Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis. Tomography. 2020; 6(2):129-138. https://doi.org/10.18383/j.tom.2020.00005

Chicago/Turabian Style

LoCastro, Eve, Ramesh Paudyal, Yousef Mazaheri, Vaios Hatzoglou, Jung Hun Oh, Yonggang Lu, Amaresha Shridhar Konar, Kira vom Eigen, Alan Ho, James R. Ewing, and et al. 2020. "Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis" Tomography 6, no. 2: 129-138. https://doi.org/10.18383/j.tom.2020.00005

APA Style

LoCastro, E., Paudyal, R., Mazaheri, Y., Hatzoglou, V., Oh, J. H., Lu, Y., Konar, A. S., Eigen, K. v., Ho, A., Ewing, J. R., Lee, N., Deasy, J. O., & Shukla-Dave, A. (2020). Computational Modeling of Interstitial Fluid Pressure and Velocity in Head and Neck Cancer Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging: Feasibility Analysis. Tomography, 6(2), 129-138. https://doi.org/10.18383/j.tom.2020.00005

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