Maryellen L. Giger, The University of Chicago
Deciphering Breast Cancer through Breast MRI, Radiomics, and Deep Learning
Argonne Physics Division Colloquium - 23 Mar 2018
11:00 AM, Building 203 Auditorium

Adapting the Precision Medicine Initiative into imaging research includes studies in both discovery and translation. Discovery is a multi-disciplinary data mining effort involving researchers such as radiologists, medical physicists, oncologists, computer scientists, engineers, and computational geneticists. Quantitative radiomic analyses and machine learning 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. The role of quantitative radiomics continues to grow beyond computer-aided detection, with AI methods being developed to (a) quantitatively characterize the radiomic 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 signatures, (c) estimate the probability of a particular disease state, and (d) explore imaging genomics association studies between the image-based features/signatures and histological/genomic data. Advances in machine learning are allowing for these computer-extracted features (phenotypes), both from clinically-driven, hand-crafted feature extraction systems and deep learning methods, to characterize a patient’s tumor via “virtual digital biopsies”. Ultimately translation of discovered relationships will include applications to the clinical assessments of cancer risk, prognosis, response to therapy, and risk of recurrence.

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