Perfusion kinetics in human brain tumor with DCE-MRI derived model and CFD analysis

Abstract Cancer is one of the leading causes of death all over the world. Among the strategies that are used for cancer treatment, the effectiveness of chemotherapy is often hindered by factors such as irregular and non-uniform uptake of drugs inside tumor. Thus, accurate prediction of drug transpor...

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Bibliographic Details
Published in:Journal of biomechanics 2017-07, Vol.59, p.80-89
Main Authors: Bhandari, A, Bansal, A, Singh, A, Sinha, N
Format: Article
Language:English
Subjects:
Algorithms
Apoptosis-inducing factor
Arterial input function
Biological Transport
Blood
Brain
Brain cancer
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - metabolism
Brain tumors
Cancer
CFD
Chemotherapy
Computational neuroscience
Contrast Media - pharmacokinetics
DCE-MRI
Death
Deposition
Extracellular Fluid - physiology
Fluid pressure
Human brain tumor
Humans
IFP
IFV
Kinetics
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical models
Models, Biological
Neuroimaging
NMR
Nuclear magnetic resonance
Perfusion
Permeability
Physical Medicine and Rehabilitation
Plasma
Porosity
Porous media
Predictions
Pressure
Resonance
Rodents
Tracer transport
Transport
Tumors
Velocity
Voxelized model
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Summary:Abstract Cancer is one of the leading causes of death all over the world. Among the strategies that are used for cancer treatment, the effectiveness of chemotherapy is often hindered by factors such as irregular and non-uniform uptake of drugs inside tumor. Thus, accurate prediction of drug transport and deposition inside tumor is crucial for increasing the effectiveness of chemotherapeutic treatment. In this study, a computational model of human brain tumor is developed that incorporates dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data into a voxelized porous media model. The model takes into account realistic transport and perfusion kinetics parameters together with realistic heterogeneous tumor vasculature and accurate arterial input function (AIF), which makes it patient specific. The computational results for interstitial fluid pressure (IFP), interstitial fluid velocity (IFV) and tracer concentration show good agreement with the experimental results. The computational model can be extended further for predicting the deposition of chemotherapeutic drugs in tumor environment as well as selection of the best chemotherapeutic drug for a specific patient.
ISSN:0021-9290
1873-2380
DOI:10.1016/j.jbiomech.2017.05.017