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Here, we utilized a living biobank of 30 histopathologically and genetically characterized PDOs, alongside a microenvironment\u2010derived from pancreatic, colon, and gastric cancers, to systematically evaluate their ability to respond to standard\u2010of\u2010care or experimental therapies and model patient outcomes. We assessed the impact of incorporating tissue\u2010matched cancer\u2010associated fibroblasts (CAFs) on treatment responses, finding that their presence not only increased chemoresistance in viability assays but significantly improved patient outcome prediction. To further enhance this predictive accuracy, we developed the Organoid Convolution Assay (OCA), a live\u2010cell imaging\u2010based approach that quantitatively captures dynamics of cell migration, clustering, and assembly during organoid formation. Mathematical modeling of these parameters enabled the significant stratification of donor tumors by stage (T0\u2013T2 vs. T3\u2013T4) and the prediction of patient clinical outcomes. 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