Podcast - Debra Patt, MD @dapattmd @USOncologyNetwork @TexasOncology #BreastCancer #Cancer #Research Quantitative Mag...

Podcast - Debra Patt, MD @dapattmd @USOncologyNetwork @TexasOncology #BreastCancer #Cancer #Research Quantitative Magnetic Resonance Imaging And Tumor Forecasting Of Breast Cancer Patient...
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Debra Patt, MD, Ph.D., MBA, FASCO, Executive Vice President at Texas Oncology a member of The US Oncology Network, Professor at Dell Medical School - The University of Texas at Austin speaks about Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting.

Link to Article:


In a community-based care context, this protocol outlines a complete data acquisition, analysis, and computational forecasting pipeline for using quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy. The methodology has been successfully used to a diverse patient group in the past. The protocol explains how to obtain the appropriate pictures, then register, segment, do quantitative perfusion and diffusion analysis, calibrate the model, and make predictions. The scanning portion of the protocol takes around 25 minutes, postprocessing takes about 2-3 hours, and the model calibration and prediction take about 10 hours per patient, depending on tumor size. A biophysical, reaction-diffusion mathematical model is used to predict the response of individual breast cancer patients to neoadjuvant therapy. The methodology yields coregistered MRI data from at least two scan sessions, which quantifies the size, cellularity, and vascular characteristics of an individual tumor. This allows for a spatially detailed forecast of how a tumor will respond to therapy in a certain patient. This protocol necessitates knowledge of image acquisition and processing, as well as numerical solution of partial differential equations.