 This study proposes a novel method for generating bioprinted tumor organoids linked to high-speed live cell interferometry, HSLCI, and machine learning-based quantitation of individual organoids. The bioprinted organoids are able to maintain their original tumor histology and gene expression profiles, while also allowing for the identification of organoids that are either sensitive or resistant to specific therapies. This information could then be used to guide rapid therapy selection. This article was authored by Peyton J. Tebin, Bowen Wang, Alexander L. Markowitz, and others.