2025
12:00 PM - 01:00 PM
Tuesday Tue
Location
Medical Physics Seminar - Kyle Lafata
Time:
12:00pm – 1:00pm Seminar & Discussion
Location:
Zoom Webinar
Webinar Registration:
https://stanford.zoom.us/webinar/register/WN_4NaqpQN7TeehRw3jg1Qk6A
Check your email for the Zoom webinar link after you have registered
Speaker
Dr. Kyle Lafata, Ph.D., Thaddeus V. Samulski Associate Professor of Radiation Oncology at Duke University
Kyle Lafata, PhD is the Thaddeus V. Samulski Associate Professor of Radiation Oncology at Duke University. His research focuses on the theory, development, and application of computational and mathematical oncology. The Lafata Laboratory interrogates cancer at different length-scales of its biological organization via high-performance scientific computing, multiscale mathematical modeling, advanced imaging technology, and the applied analysis of stochastic partial differential equations. Recent applications include tumor topology, cell state dynamics, immune microenvironment, molecular insights into tumor heterogeneity, digital twins of tumors, and biologically-guided adaptive treatment strategies. Dr. Lafata earned his PhD in Medical Physics in 2018 and trained as a postdoctoral fellow at the U.S. Department of Veterans Affairs within the Big Data Scientist Training Enhancement Program. Since 2018, Dr. Lafata has authored over 65 peer-reviewed papers, delivered over 30 invited talks, and presented at more than 100 national conferences. In addition to his research and laboratory efforts, Dr. Lafata teaches both Introduction to Radiation Biology and Techniques in Mathematical Oncology within the Medical Physics Graduate Program at Duke University.
Abstract
Head and neck cancer presents a major therapeutic challenge because factors contributing to treatment resistance are poorly understood. Heterogeneity in these tumors spans multiple length-scales of biological organization, including tissue, cellular, and molecular levels. Characterization of these domains is essential to overcoming therapeutic resistance and guiding personalized treatment strategies. This seminar talk will focus on computational tumor phenotyping strategies and multiscale mathematical modeling of head and neck cancer treatment resistance and immune dysregulation. He will demonstrate how imaging, digital pathology, and spatial transcriptomics enable a multiscale representation of tumor appearance and behavior. By integrating physics-informed, mathematical tumor models (theory) with image-based, data-driven solutions (observables), he will demonstrate that these techniques can capture both clinically relevant and biologically-sound phenomena. Overarching illustrating examples will include radiation-induced changes in tumor dynamics, single-cell evaluation of the tumor immune microenvironment and immune response, molecular insight into tumor heterogeneity, and biologically-guided adaptive treatment strategies.