Journal article
Chengyue Wu, D. Hormuth, Todd A. Oliver, F. Pineda, G. Lorenzo, G. Karczmar, R. Moser, T. Yankeelov
IEEE Transactions on Medical Imaging, 2020
IEEE Transactions on Medical Imaging, 2020
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Wu, C., Hormuth, D., Oliver, T. A., Pineda, F., Lorenzo, G., Karczmar, G., … Yankeelov, T. (2020). Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Transactions on Medical Imaging.
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Wu, Chengyue, D. Hormuth, Todd A. Oliver, F. Pineda, G. Lorenzo, G. Karczmar, R. Moser, and T. Yankeelov. “Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics.” IEEE Transactions on Medical Imaging (2020).
MLA
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Wu, Chengyue, et al. “Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics.” IEEE Transactions on Medical Imaging, 2020.
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@article{chengyue2020a,
title = {Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics},
year = {2020},
journal = {IEEE Transactions on Medical Imaging},
author = {Wu, Chengyue and Hormuth, D. and Oliver, Todd A. and Pineda, F. and Lorenzo, G. and Karczmar, G. and Moser, R. and Yankeelov, T.}
}
Abstract
The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( ${P} =0.028$ ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology’s utility for the quantitative characterization of breast cancer.
