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Abstract #1978

Fluid mechanics based quantitative transport mapping network (QTMnet) for predicting treatment response of breast cancer from DCE MRI

QIhao Zhang1,2, Dominick Romano3, Renjiu Hu2, Benjamin Weppner2, Thanh Nguyen2, Pascal Spincemaille1, and Yi Wang2
1Weill Cornell Medicine, New York, NY, United States, 2Weill Cornell Medicine, New York, NY, United States, 3Cornell University, New York, NY, United States

Synopsis

Keywords: Breast, DSC & DCE Perfusion, quantitative analysis, AI processing, breast MRI

Motivation: There lacks a non-invasive breast cancer treatment response prediction method from MRI images.

Goal(s): To test the feasibility of using perfusion parameter reconstructed from QTMnet processing of DCE MRI to predict breast cancer treatment response.

Approach: QTMnet is trained on synthetically generated perfusion data and tested on 41 breast DCE MRI images.

Results: $$$PS$$$ and $$$V_e$$$ map from QTMnet can predict whether breast cancer has complete treatment responses.

Impact: QTMnet can be coupled into clinical breast cancer practice to predict treatment response.

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