3D-bioprinted cancer models for prediction of nanomedicine response driven by H&E-based AI algorithm

Introduction: Many drugs show promising results in laboratory research but eventually fail to show clinical benefit. One main reason for this translational gap is that the cancer models used are inadequate. Most models lack the complex tumor-stromal-immune cell interactions with their tumor microenvironment (TME) that are required for progression. Conventional 2D models, where cells grow on rigid plastic plates mainly as mono-cultures, fail to recapitulate these complex interactions. Hence, we developed two platforms of 3D cancer models that better mimic the TME, a 3D tumoroid and a 3D vascularized model.