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Abstract #2319

Background Parenchymal Enhancement (BPE) classification on Breast MRI using Deep Learning

Sarah Eskreis-Winkler1, Katja Pinker1, Donna D'Alessio1, Katherine Gallagher1, Nicole Saphier1, Danny Martinez1, Elizabeth Sutton1, and Elizabeth Morris1
1Memorial Sloan Kettering Cancer Center, New York, NY, United States

Breast cancer risk in high risk women is significantly increased for those with high background parenchymal enhancement (BPE) compared to low BPE, yet there is only fair to moderate inter-rater agreement for BPE assessment among radiologists, limiting the use of BPE as a marker of cancer risk. We developed a deep learning algorithm that classifies BPE with high accuracy. The algorithm works best when sub-MIPs, not MIPs, are used as network inputs. The algorithm has potential to autopopulate breast MRI reports in our breast imaging clinic, and ultimately to standardize BPE as a marker of breast cancer risk.

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