Breast Cancer Imaging with MRI: Challenges and Opportunities
Breast cancer remains the leading cause of cancer-related death in women, despite recent advances in cancer detection and treatment. MRI is the most sensitive imaging modality for identifying aggressive cancer types, yet it has been largely underutilized. This talk will focus on key challenges that have limited the broader adoption of breast MRI, particularly the need for fast imaging, robust quantitative data analysis, and accurate cancer risk assessment. We will also discuss how artificial intelligence methods have been utilized to address those challenges as well as their future potential.
Bio: Sungheon Gene Kim is a professor of biomedical engineering in radiology at Weill Cornell Medical College, Cornell University. He received his Ph.D. in biomedical engineering from the University of Southern California and completed postdoctoral training at the National Institute of Health and the University of Pennsylvania before joining the faculty of NYU School of Medicine in 2008. In 2020, he moved to Weill Cornell Medical College, where his research has focused on developing quantitative MRI techniques to probe the tumor microenvironment. Kim’s group pursue novel imaging approaches in three interrelated areas: diffusion MRI for tumor microstructure imaging; dynamic contrast-enhanced (DCE) MRI for perfusion imaging to characterize tumor vascular and cellular properties; and MR spectroscopic imaging to investigate the role of adipose tissue in cancer development and progression. Together, these efforts aim to advance MRI a quantitative, noninvasive tool for precision oncology.