Abstract #0599

Patient-specific characterization of breast tumor-associated flow using image-guided computational fluid dynamics

Chengyue Wu¹, David A. Hormuth², Todd A. Oliver², Federico Pineda³, Gregory S. Karczmar³, Robert D. Moser², and Thomas E. Yankeelov^1,2,4,5

¹Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, United States, ²Institute for Computational and Engineering Sciences, University of Texas at Austin, Austin, TX, United States, ³Department of Radiology, University of Chicago, Chicago, IL, United States, ⁴Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, United States, ⁵Department of Oncology, University of Texas at Austin, Austin, TX, United States

Tumor blood supply and interstitial flow play an essential role in tumor growth, invasion, and treatment response. In this contribution, we employ quantitative DCE-MRI and DWI data to constrain a patient-specific, computational fluid dynamics model of blood flow within breast tumors. To the best of our knowledge, this represents the first attempt at employing non-invasive imaging data to enable quantitative--and spatially resolved--characterization of physiological properties related to vascular and interstitial pressure and velocity. The approach is general and can be used to evaluate cancer and other diseases.

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