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AI in Medical Imaging of Breast Cancer and COVID-19, with updates on MIDRC

Maryellen Giger, Professor of Radiology, University of Chicago

Wegmans Hall 1400

Abstract: Artificial Intelligence in medical imaging involves research in task-based 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 cancer diagnostics and treatments. Beyond human-engineered features, deep convolutional neural networks (CNN) are being investigated in the diagnosis of disease on radiography, ultrasound, and MRI.  The method of extracting characteristic radiomic features of a lesion and/or background can be referred to as “virtual biopsies”.  Various AI methods are evolving as aids to radiologists as a second reader or a concurrent reader, or as a primary autonomous reader.  This presentation will discuss the development, validation, database needs, and ultimate future implementation of AI in the clinical radiology workflow including examples from breast cancer and COVID-19.  In addition, aspects of MIDRC (midrc.org) will be discussed, including methods for collecting, de-identifying, curating, harmonizing, and sequestering imaging data.

Bio: Maryellen Giger, Ph.D. is the A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago.  She has been working, for decades, on computer-aided diagnosis/machine learning/deep learning in medical imaging for cancer, thoracic diseases, neuro-imaging, and other diseases diagnosis and management.  Her AI research in breast cancer for risk assessment, diagnosis, prognosis, and therapeutic response has yielded various translated components, and she has used these “virtual biopsies” in imaging-genomics association studies. She has extended her AI in medical imaging research to include the analysis of COVID-19 on CT and chest radiographs, and is contact PI on the NIBIB-funded Medical Imaging and Data Resource Center (MIDRC; midrc.org), which, as of August 2022, has ingested more than 150,000 medical imaging studies for use by AI investigators. Giger has more than 260 peer-reviewed publications and has more than 30 patents, and has mentored over 100 graduate students, residents, medical students, and undergraduate students. Giger is a former president of AAPM and of SPIE; was a member of the NIBIB Advisory Council of NIH; and is the Editor-in-Chief of the Journal of Medical Imaging.  She is a member of the National Academy of Engineering (NAE), a recipient of the AAPM William D. Coolidge Gold Medal, a recipient of the SPIE Director’s Award and the SPIE Harrison H. Barrett Award in Medical Imaging, a recipient of the RSNA’s Honored Educator Award and the RSNA’s Outstanding Researcher Award, and is a Fellow of AAPM, AIMBE, SPIE, SBMR, IEEE, IAMBE, and COS.  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.  Giger was cofounder of Quantitative Insights [now Qlarity Imaging], which produces QuantX, the first FDA-cleared, machine-learning driven CADx (AI-aided) system.

Refreshments will be provided.