Shape factor analysis as a quantitative framework for assessing spheroid and organoid morphology and invasiveness

Morphological changes in spheroids and organoids are widely used as in vitro indicators of healthy and diseased tissue function, but selecting appropriate methods to quantify these changes remains challenging. Shape factors (or shape descriptors) are dimensionless metrics often computed using ImageJ/FIJI; however, their ability to classify specific morphological features can vary. To address this challenge, we developed a clinically inspired, custom MATLAB algorithm to quantify the variance in radial lengths of invasive protrusions in spheroids and organoids. We then compared the advantages and limitations of this approach with conventional ImageJ/FIJI shape descriptors to guide users in selecting the most appropriate method for classifying spheroid and organoid morphology in their specific settings. To this end, we first analyzed digital phantoms and then performed the same comparisons using images from experimental spheroid and organoid datasets. 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.