Immediate Implications
Enter Shannon Agner, a Ph.D. student in Rutgers’ Laboratory for Computational Imaging and Bioinformatics, who is working with colleagues on innovative methods for computer-aided detection and diagnosis of breast cancer through MRIs. These methods, called “textural kinetics,” use pattern recognition and a novel analysis of breast lesions to determine whether a biopsy is necessary, or even to detect specific molecular types of breast cancer on the MRI. If the analysis indicates a particularly aggressive form of cancer, that information can be used to help the doctor decide what treatment is appropriate and whether quick action is a must.
By working with data from individuals undergoing MRIs for the possibility of breast cancer, Shannon is already seeing how her work holds potential to assist in diagnosing cancer. “I can see the immediate implications, from the bench [research lab] to the clinic.”
Assisting Physicians
“The analysis that we do is never meant to replace the physician,” she adds. “It’s there to guide the physician’s decision making.”
Shannon is well positioned to understand the needs of doctors. A joint M.D./Ph.D. student, Shannon is part of a program at Robert Wood Johnson Medical School for students interested in acquiring expertise in both medical research and clinical practice. Her dissertation committee includes an interdisciplinary mix of experts, including two Rutgers biomedical engineering professors, an oncologist from the Cancer Institute of New Jersey, and a pathologist and a radiologist, both from the University of Pennsylvania, which is the source of Shannon’s MRI data.
Support and Inspiration
Shannon chose to study in Rutgers–New Brunswick’s Department of Biomedical Engineering due to her interest in imaging and the type of work conducted at the Laboratory for Computational Imaging and Bioinformatics, which has immediate relevance to the lives of patients. She praises Anant Madabhushi, who is her adviser and the director of the lab, for being supportive and inspiring. “I thought I worked hard until I joined Anant’s lab,” she says. “Just when you think you’ve poured every ounce of yourself into a project, you realize that Anant is working just as hard on your project. Whether it’s reviewing conference abstracts or papers we’ve written or making the time to meet with us about data, he really puts 110 percent into all of the projects in the lab.”
