Areas of Research in Mathematical Oncology

The Division of Mathematical Oncology is engaged in numerous areas of collaborative research, including:

  • Modeling tumor / immune system dynamics, specifically how cancer cells evolve to hide from immune cell recognition, and if that process can be halted or reversed
  • Applying evolutionary theory to understand leukemia development, particularly the growth and proliferation of treatment-resistant “leukemia stem cells”
  • Using imaging data, including positron emission tomography, or PET, and magnetic resonance imaging, or MRI, data to help predict a cancer’s growth and response to treatment, particularly for cancers that are hard to observe directly — such as brain tumors
  • Integrating complex data sets to optimize therapies for best clinical and quality-of-life outcomes
  • Improve screening and early detection efforts by identifying and quantifying factors that contribute to cancer risk
  • Enhancing clinical trials through better selection and enrollment of candidates — patients who are most likely to benefit from the tested therapy

For more information about Mathematical Oncology research, or if you are interesting in partnering with the division for a study, please contact: