Engineering Commons^™

Quantitative Imaging Reveals Heterogeneous Growth Dynamics And Treatment-Dependent Residual Tumor Distributions In A Three-Dimensional Ovarian Cancer Model, Jonathan P. Celli, Imran Rizvi, Conor L. Evans, Adnan O. Abu-Yousif, Tayyaba Hasan

Dartmouth Scholarship

Three-dimensional tumor models have emerged as valuable in vitro research tools, though the power of such systems as quantitative reporters of tumor growth and treatment response has not been adequately explored. We introduce an approach combining a 3-D model of disseminated ovarian cancer with high-throughput processing of image data for quantification of growth characteristics and cytotoxic response. We developed custom MATLAB routines to analyze longitudinally acquired dark-field microscopy images containing thousands of 3-D nodules. These data reveal a reproducible bimodal log-normal size distribution. Growth behavior is driven by migration and assembly, causing an exponential decay in spatial density concomitant with …