Estimation of model parameters used to predict treatment response in breast cancer from dynamic contrast-enhanced MRI data using novel mathematical software

9 Jul 2018, 18:00
Holme Building/--The Refectory (University of Sydney)

Holme Building/--The Refectory

University of Sydney

Board: 509
Poster Presentation Other Mathematical Biology Poster Session


David Ekrut (University of Texas at Austin)


Specific biomarkers can be identified in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) breast scans and quantified using pharmacokinetic models that return estimates of parameters related to tissue physiology including vessel perfusion and permeability ($K^{trans}$), the extravascular-extracellular volume fraction ($v_e$), the plasma volume fraction ($v_p$), and the efflux constant ($k_{ep}$). In particular, $K^{trans}$ and $k_{ep}$ have been shown to be effective at predicting the response of cancer patients to treatment. Two fundamental issues in the field of DCE-MRI is the lack of standardization of the analysis and characterizing the time rate of change of the concentration of contrast agent in the vascular (the so-called “arterial input function” or AIF). We have recently developed a method for estimating accurate AIFs for the individual patients and associated software to automate the estimation of model parameters from DCE-MRI data taken from breast cancer patients using data that can be acquired routinely in community-based imaging centres.

Primary authors

David Ekrut (University of Texas at Austin) Ms Chengyue Wu (UT Austin) Dr Jack M. Virostko (UT Austin) Dr Anna G. Sorace (University of Texas at Austin) Dr Thomas E. Yankeelov (University of Texas at Austin)

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