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Abstract #2373

Comparison of breast BPE segmentation methods for early prediction of response to treatment

Alex Nguyen1, Fredrik Strand2, Vignesh Arasu1, Wen Li1, Natsuko Onishi1, Jessica Gibbs1, Bonnie N Joe1, Laura J Esserman3, The I-SPY2 Investigator Network4, David C Newitt1, and Nola M Hylton1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden, 3Department of Surgery, University of California, San Francisco, San Francisco, CA, United States, 4Quantum Leap Healthcare Collaborative, San Francisco, CA, United States

Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging and there is no agreed upon standard. This study compares the results of three fully automated segmentation methods for early prediction of pathologic complete response (pCR) following neoadjuvant treatment. We evaluated three different sub-volumes of interest segmented from DCE-MRI: full stack, half stack, and center 5 slices. The differences between methods were assessed and a univariate logistic regression model was implemented to determine predictive performance of each segmentation method.

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