AI-powered deep phenotyping: Vision transformers quantify vessel normalization and immune efficacy in a 3D vascularized tumor-on-a-chip

​​Introduction: Dysfunctional, chaotic tumor vasculature creates a hostile physical barrier to immune infiltration, severely limiting the efficacy of immunotherapies. Conventional models fail to replicate this structural complexity or the dynamic interplay between vessels and immune cells. To address this, we validated a high-throughput, AI-driven vascularized tumor microenvironment (TME) platform designed not only to model this barrier but to rigorously quantify tumor-induced angiogenesis, vessel normalization, and downstream immune cell behaviors. Methods: Using the Qureator's microphysiological system (MPS), we generated a patient-derived gastric cancer model with perfusable vasculature. To validate predictive utility, models were treated with FDA-approved anti-angiogenic agents (e.g., Ramucirumab, a second-line therapy for gastric cancer) and a panel of vessel normalization reagents. We developed a customized, machine learning-powered Vision Transformer (ViT) pipeline to analyze immunofluorescence images. Unlike traditional morphological tools, our ViT approach utilizes self-attention mechanisms to capture global dependencies, allowing for the rigorous quantification of high-dimensional deep phenotyping features. Results: The platform established a clinically relevant TME where Ramucirumab treatment significantly reduced peritumoral vessel density, accurately recapitulating clinical anti-angiogenic effects. Beyond simple density metrics, our ViT-based deep phenotyping identified distinct phenotypic clusters among different normalization reagents based on complex, non-linear features such as vessel tortuosity, branch density, and pericyte coverage. Crucially, this high-dimensional AI analysis revealed a direct functional correlation: reagents that induced a specific "normalized" vascular signature were strongly associated with a measurable increase in T cell-mediated killing efficiency, linking restored vascular morphology to improved therapeutic outcomes. Conclusion: This AI-enabled TME platform represents a powerful New Approach Methodologies (NAMs), integrating deep learning with complex biology to perform "deep phenotyping" of the tumor microenvironment. By precisely linking vascular morphology to functional immune outcomes, it accelerates the discovery of therapies that restore the TME to a treatment-responsive state.