Meeting Banner
Abstract #2490

Distinguishing Molecular Subtypes of Breast Cancer Based on Computer-Aided Diagnosis of DCE-MRI

Shannon Agner1, Mark Rosen2, Sarah Englander2, Diana Sobers1, Kathleen Thomas2, John Tomaszewski3, Michael Feldman3, Shridar Ganesan1, Mitchell Schnall2, Anant Madabhushi1

1Biomedical Engineering, Rutgers University, Piscataway, NJ, United States; 2Radiology, University of Pennsylvania, Philadelphia, PA, United States; 3Pathology, University of Pennsylvania, Philadelphia, PA, United States

Previous studies based on visual inspection of breast tumors suggest that molecular subtypes of breast cancer are associated with distinct imaging phenotypes on DCE-MRI. In this study, we develop a computer-aided diagnosis tool that utilizes textural kinetics, an attribute that captures time related changes in internal lesion texture, to distinguish between 20 triple negative (estrogen receptor (ER) negative/ progesterone receptor (PR) negative/ human epidermal growth factor (HER2) receptor negative) and 21 ER positive tumors. Our CAD system was found to outperform classifiers that were driven by morphology, signal intensity kinetics, peak contrast texture, and pharmacokinetic parameters.