Leadership Distinguished Lecture Series: Deciphering Breast Cancer with Imaging, Genomics, & Big Data: Imaging Phenotypes in Breast Cancer Risk Assessment, Diagnosis, Prognosis, and Response to Therapy

Date(s) - 11/10/2014
4:00 pm

Maryellen Giger, Ph.D., A.N. Pritzker Professor of Radiology and the College Vice-Chair for Basic Science Research, University of Chicago


Quantitative image analysis (QIA) and computer-aided diagnosis (CAD) methods (i.e., computerized methods of analyzing digital breast images: mammograms, ultrasound, and magnetic resonance images) can yield novel image-based tumor characteristics (i.e., signatures that may ultimately contribute to the design of patient-specific breast cancer treatments). The role of QIA continues to grow. With computer-aided detection (CAD) of breast cancer, the aim was to provide a ‘second opinion’ to aid the radiologist in locating suspicious regions within screening mammograms. Today, the role of QIA/CAD is expanding beyond screening programs towards applications in risk assessment, diagnosis, prognosis, and response to therapy as well as in data mining to discover relationships of lesion characteristics as they apply to disease states. With QIA, computerized methods are being developed to (a) quantitatively characterize the features of a suspicious region or tumor, e.g., those describing tumor morphology or function, (b) merge the relevant features into diagnostic, prognostic, or predictive image-based biomarkers, (c) estimate the probability of a particular disease state, (d) retrieve similar cases, (e) compare the tumor in question to thousands of other breast tumors, and (f) explore the complex relationships among image-based tumor characteristics across large populations and association studies between the image-based signatures (i.e, image-based phenotypes) and histological/genomic data for imaging genomics.

Brief Bio:


Maryellen L. Giger, Ph.D. is the A.N. Pritzker Professor of Radiology/Medical Physics and the College at the University of Chicago.  She is also Vice-Chair of Radiology for Basic Science Research and Director of the BSD Imaging Research Institute.  She is 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 25 years, she has conducted research on computer-aided diagnosis and quantitative image analysis in the areas of breast cancer, lung cancer, prostate cancer, and bone diseases, including imaging genomics.  She has also served on various NIH study sections, is a former president of the American Association of Physicists in Medicine, is a member of the National Academy of Engineering, a Fellow of AAPM, AIMBE, and SPIE, a Board Member of SPIE, a Board Member of CAMPEP, and the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging.  She has more than 170 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 image-based analyses of breast cancer for risk assessment, diagnosis, prognosis, response to therapy, and biological discovery has yielded various translated components.  She is leading research on the image analysis of multi-scale imaging data from breast MRI and digital pathology in the assessment of breast cancer subtypes (yielding image-based phenotypes).