Distinguished Medical Engineering Seminar
Quantitative Radiomics and Machine Learning in Breast Cancer Image Analysis
Quantitative machine learning in imaging for precision medicine involves research in discovery, predictive modeling, and robust clinical translation. Quantitative radiomic analyses, an extension of computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods, are yielding novel image-based tumor characteristics, i.e., signatures that may ultimately contribute to the design of patient-specific breast cancer diagnostics and treatments. Beyond human-engineered features, deep convolutional neural networks (CNN) are being investigated in the diagnosis of breast tumors on mammography, ultrasound, and breast MRI. The method of extracting characteristic radiomic features of a lesion and/or background can be referred to as "virtual biopsies". In addition, with quantitative features, one can explore imaging genomics association studies between the image-based features/signatures and histological/genomic data leading to precision medicine.
Biography: Maryellen L. Giger, Ph.D. is the A.N. Pritzker Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She is also the Vice-Chair of Radiology (Basic Science Research) and the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/ Chair of the Committee on Medical Physics at the University. For over 30 years, she has conducted research on computer-aided diagnosis, including computer vision, machine learning, and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases. Over her career, she has served on various NIH, DOD, and other funding agencies' study sections, and is now a member of the NIBIB Advisory Council of NIH. She is a former president of the American Association of Physicists in Medicine, was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging, and is the current President of SPIE (the International Society of Optics and Photonics). She is a member of the National Academy of Engineering (NAE) and was awarded the William D. Coolidge Gold Medal from the American Association of Physicists in Medicine, the highest award given by the AAPM. She is a Fellow of AAPM, AIMBE, SPIE, SBMR, and IEEE, a recipient of the EMBS Academic Career Achievement Award, and is a current Hagler Institute Fellow at Texas A&M University. In 2013, Giger was named by the International Congress on Medical Physics (ICMP) as one of the 50 medical physicists with the most impact on the field in the last 50 years. IN 2018, she received the iBIO iCON Innovator award. She has more than 200 peer-reviewed publications (over 300 publications), has more than 30 patents and has mentored over 100 graduate students, residents, medical students, and undergraduate students. Her research in computational image-based analyses of breast cancer for risk assessment, diagnosis, prognosis, and response to therapy has yielded various translated components, and she is now using these image-based phenotypes, i.e., these "virtual biopsies" in imaging genomics association studies for discovery. She is a cofounder, equity holder, and scientific advisor of Quantitative Insights, Inc., which started through the 2009-2010 New Venture Challenge at the University of Chicago. QI produces QuantX, the first FDA-cleared, machine-learning driven system for cancer diagnosis (CADx).