Speaker
Description
Three-dimensional (3D) tumor spheroids, reflecting avascular microregions within a tumor, are preclinical cell culture systems for assessing the impact of radio(chemo)therapy. However, spheroid experiments remain laborious, and determining long-term radio(chemo)therapy outcomes is challenging. Mathematical models of spheroid dynamics have the potential to enhance the informative value of experimental data, and can support study design. We present an effectively one-dimensional mathematical model based on the cell dynamics within and across radial spheres which fully incorporates the 3D dynamics of tumor spheroids by exploiting their approximate rotational symmetry and demonstrate that this radial-shell (RS) model reproduces experimental spheroid growth curves. In parallel, we develop a (semi-) automated spheroid analysis pipeline to evaluate thousands of microscopy images per treatment arm. The pipeline integrates automated spheroid segmentation and classification of therapeutic outcome using statistical and machine learning algorithms. Model integration into the data-driven computational pipeline enables the analysis of complex radiobiological data sets and facilitates biologically optimized, innovative solutions for radiation therapy.
