Past Event: Oden Institute Seminar

Radiomic machine learning for precision breast cancer prevention

Aimilia Gastounioti, Research Associate, Department of Radiology, Computational Breast Imaging Group, Center for Biomedical Image Computing and Analytics, University of Pennsylvania

Abstract

Breast cancer risk assessment has become increasingly important for forming tailored breast cancer screening and prevention strategies. An emerging approach to evaluate breast cancer risk profiles more accurately and help better guide personalized patient care is the incorporation of computational imaging phenotypes. In this talk, I will discuss novel computational approaches that leverage radiomic machine learning in breast cancer risk estimation from various complementary viewpoints. First, I will describe a systematic comparative study on large-population data involving the two types of images generated from digital mammography towards investigating potential differences in quantitative measurements which would have subsequent implications in related interpretation. I will then present a novel computational framework which allows breast anatomy to drive breast imaging phenotyping of breast cancer risk. Third, I will discuss the use of the cutting-edge deep learning technologies to better capture breast parenchymal complexity patterns which are associated with breast cancer risk. Bio Aimilia Gastounioti PhD, is a Research Associate in the Department of Radiology at the University of Pennsylvania (UPenn), where she is a member of the Computational Breast Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA). Aimilia received her Ph.D. in Biomedical Engineering from the National Technical University of Athens in 2014, part of which was performed in collaboration with the Ecole Centrale de Paris. She has also received her Clinical Research Certificate from UPenn for training in clinical epidemiology, biostatistics and translational research. Her research interests focus on translational biomedical imaging research with a primary focus on radiomic machine learning related to cancer risk-prediction, diagnosis and prognosis. She has co-authored 18 journal articles, 2 book chapters, 25 conference proceedings papers, as well as 16 abstracts in premier scientific meetings. She has been leading a 3-year Susan G. Komen foundation fellowship as a PI and she has also served as co-PI in two seed grants funded by internal sources at UPenn. Her research work has been awarded by the Engineering in Medicine & Biology (EMB) Greece Chapter (2014) and has also been included in Research Highlights of the IEEE Journal of Biomedical Health Informatics (2015), the SPIE Medical Imaging Conference (2017) and the 30th Anniversary AACR Special Conference on Convergence: Artificial Intelligence, Big Data, and Prediction in Cancer (2018). Aimilia is an Associate member of the American Association for Cancer Research (AACR) and the Institute for Translational Medicine and Therapeutics (ITMAT) at the University of Pennsylvania.