Computational imaging phenotyping as a biomarker for precision care of breast cancer

Despina Kontos
Associate Professor of Radiology, and Director the Computational Breast Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA), Radiology Department
University of Pennsylvania
Location: 
SERC 306
Date: 
Thursday, December 6, 2018 - 11:00
As new options for breast cancer screening, early detection and treatment become available it is essential to provide accurate, clinically relevant methods to identify women that would benefit most from specific approaches. An emerging approach to improve individualized risk assessment in clinical decision making for breast cancer is the incorporation imaging biomarkers. Our studies with multi-modality breast imaging suggest that imaging can play an important role for personalizing patient care. Quantitative measures of breast density and parenchymal texture can improve the prediction accuracy of breast cancer risk estimation models and potentially, help guide personalized breast cancer screening protocols. Tumor phenotypic characteristics, such as shape, morphology, and heterogeneity of contrast enhancement kinetics from magnetic resonance imaging are indicative of molecular subtypes of breast cancer and correlate with the probability of future recurrence. Such phenotypic tumor imaging markers can also be used as surrogates for treatment response, including neo-adjuvant chemotherapy, and help identify earlier patients that are most likely to respond to treatment. This emerging evidence therefore suggests a new clinical paradigm that will necessitate integrating multi-modality imaging biomarkers with genomics, histopathology, and clinical risk factors to assess individualized patient risk and help better guide clinical decisions for breast cancer. This talk will provide an overview of investigations currently on-going at our institution that include digital mammography, digital breast tomosynthesis and magnetic resonance imaging biomarkers and their potential clinical utility in guiding personalized screening, prevention, and treatment approaches for breast cancer.