Shape Factor Analysis as a Quantitative Framework for Assessing Spheroid and Organoid Morphology and Invasiveness

Morphological changes of spheroids and organoids are widely used as in vitro indicators of healthy and diseased tissue functions; however, quantitative methods to classify spheroid and organoid morphology are limited. In clinical breast imaging, radiologists use tumor shape as a prognostic marker, with irregular margins associated with invasive disease and increased malignancy. Here, we adapted this approach for translational research and developed a custom MATLAB algorithm to quantify the variance in radial lengths of invasive protrusions in spheroids and organoids. First, we analyzed digital phantoms by both ImageJ/FIJI shape descriptors and our radial length analysis to evaluate the capabilities of each measurement method. Subsequently, we performed the same comparisons with images from experimental spheroid and organoid datasets. We demonstrate that multivariate shape factor analysis, including radial length analysis, enables more reliable and comprehensive quantification of spheroid and organoid morphologies than standard shape descriptors alone. By enabling numerical morphological readouts, shape factor analysis can enhance phenotypic profiling of spheroids and organoids and provide valuable metrics for in vitro studies including high-throughput and drug screening workflows.