Practical digital twins for patients who are not average

Over the last decade our team has developed a set of partial differential equations that capture key features of tumor growth and treatment response related to tumor cell migration, proliferation, treatment response, and tissue mechanical properties. These models can be calibrated with widely-available medical imaging data to accurately predict the spatio-temporal changes of solid tumors in response to both radiation and systemic therapies. We will summarize those results for cancers of the brain and breast, and then show how these models power digital twins to identify alternative therapeutic regimens that are hypothesized to outperform the standard-of-care interventions. 

Bio
https://www.bme.utexas.edu/people/faculty-directory/yankeelov