Recent publications

AI-driven imaging research improves tumor segmentation accuracy

New research by CCRI member Dr. Neil Duggar, associate professor of radiation oncology at UMMC, and affiliate member Dr. Haifeng Wang, associate professor and co-graduate coordinator at Mississippi State University, has been published highlighting how deep learning models can improve the diagnosis and treatment of head and neck cancer. The study demonstrates how optimizing key training settings in AI models, using a novel Bayesian Optimization-derived

Scheduling approach, can significantly enhance the accuracy of tumor segmentation from CT and PET imaging. This work adds to growing evidence that advanced AI tools can strengthen cancer imaging, support clinical decision-making, and improve patient care. Their research is in the journal Computers in Biology and Medicine.  

Research highlights glycopolymers as platform for next-generation care

Research by CCRI member Dr. Thomas Werfel, associate professor of biomedical engineering and affiliate associate professor of chemical engineering and biomolecular sciences, and associate director of the Glycoscience Center of Research Excellence (GlyCORE) at the University of Mississippi, has been published highlighting the promise of glycopolymers as a next-generation platform for cancer immunotherapy. The research, published in the Journal of Controlled Release, outlines how these highly tunable materials can precisely modulate immune responses—supporting applications ranging from cancer vaccines and antibody recruitment to tumor microenvironment reprogramming and diagnostic sensing. By mimicking natural immune interactions while improving stability and control, glycopolymers may help overcome key limitations of current immunotherapies and move the field closer to more personalized, effective cancer treatments.