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Abstract #4438

Breast Cancer Diagnosis: A Multiparametric Magnetic Resonance Imaging Model with Dynamic Contrast Enhanced and Diffusion Weighted Imaging

Katja Pinker-Domenig1, Michelle Zhang1, Joao V Horvat1, Blanca Bernard-Davila1, Rosa Elena Ochoa-Albiztegui1, Elisabeth A Morris1, Sunitha Thakur1, Pascal AT Baltzer2, and Thomas H Helbich2

1Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria

To develop a multiparametric MRI model incorporating the ACR BI-RADS recommended descriptors for DCE-MRI, T2-weighted and DW imaging biomarkers for accurate breast cancer diagnosis. A multivariate logistic regression analysis of multiparametric MRI data from 210 breast tumors was performed to determine parameters that jointly predicted malignancy. A multiparametric MRI model incorporating quantitative and qualitative for DCE-MRI [mass margins (p=0.0012), initial EH (p=0.422) and delayed enhancement (0.0065)] and DW imaging biomarkers [ADCmean (p=0.0031)] enables an accurate breast cancer diagnosis. Results indicate that to maximize diagnostic accuracy a multiparametric MRI approach with DWI and DCE sequences must be considered.

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