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Identification of morphological and hemodynamic biomarkers for tumor vascular perfusion through mathematical modeling and high‐resolution imaging
Author(s) -
Stamatelos Spyros K,
Kim Eugene,
Pathak Arvind P,
Popel Aleksander S
Publication year - 2013
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.27.1_supplement.685.12
Subject(s) - hemodynamics , blood flow , medicine , perfusion , pathology , biomedical engineering , computer science , radiology
Tumor angiogenesis is a hallmark of cancer, and involves the process of blood vessel formation from pre‐existing host and tumor vascular networks. Tumor vascularity and blood flow are chaotic and heterogeneous, making delivery of therapeutic agents to solid tumors inefficient. Image‐based mechanistic hemodynamic modeling can facilitate the understanding of tumor blood perfusion and the design of drug delivery and anti‐angiogenic therapy strategies. The experimental protocol involves the development of breast cancer xenografts in mice. Ex‐vivo high resolution imaging is conducted using micro‐computed tomography, and 3‐D image reconstruction and segmentation leads to a “wide‐area” mapping of the vasculature. This process is highly anisotropic involving complex geometries, and it was necessary to develop an image‐bioinformatics algorithm to ensure connectivity among vessels. This methodology includes a 3‐D tracing algorithm which optimizes the geometry based on local cues. The mathematical formulation for blood flow is based on Poiseuille‐like law for pressure drop and nonlinear rheological equations for local apparent viscosity and hematocrit. Comparison of tumor regions across samples and time points, based on a number of morphological and hemodynamic measures, allows the identification of biomarkers for drug delivery. Supported by NIH grant R01 CA138264 and Komen Foundation Grant KG090640.