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Towards Virtual Trials in Radiation Oncology: Computational Tumor Phenotyping, Multiscale Mathematical Modeling, and In Silico Cancer Biology

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Date: February 16, 2024

Speakers: Kyle Lafata, PhD

Tumors are complex systems that span different physical length-scales of biological organization (e.g., tissue, cellular, molecular, etc.). Characterization of these domains is essential to advancing our understanding of cancer, guiding personalized treatment strategies, and improving patient outcomes. Radiological imaging, digital pathology, and Next-Generation Sequencing enable a comprehensive understanding of tumor appearance and behavior at different length-scales. In addition, in silico cancer biology can help integrate information and interactions across these length-scales to provide a better description of the system’s behavior. This talk focuses on computational tumor phenotyping, multiscale mathematical modeling, and in silico cancer biology. By integrating in silico tumor models (theory) with image-based, data-driven solutions (observables), we demonstrate that these techniques can capture both clinically relevant and biologically-sound phenomena. Illustrating examples include radiation-induced changes in tumor dynamics, single-cell evaluation of the tumor immune microenvironment, molecular insight into tumor heterogeneity, and biologically guided adaptive treatment strategies.