Computational Tumor Microenvironment Modeling Using Artificial Intelligence and Digital Twins

Abstract

The tumor microenvironment (TME) is increasingly recognized as a critical determinant of cancer initiation, progression, metastasis, therapeutic response, and clinical outcomes. Rather than functioning as isolated populations of malignant cells, tumors exist within dynamic ecosystems composed of immune cells, stromal fibroblasts, endothelial cells, extracellular matrix components, cytokines, metabolites, vascular networks, and diverse signaling pathways that collectively influence disease evolution. Recent advances in artificial intelligence (AI), systems biology, computational oncology, digital twins, spatial multiomics, computational pathology, and multimodal biomedical data integration have enabled comprehensive computational modeling of the tumor microenvironment. AI-driven digital twins create continuously evolving virtual representations of individual tumors by integrating radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, immunomics, laboratory biomarkers, wearable technologies, and longitudinal clinical information to simulate tumor–host interactions and therapeutic responses. Machine learning, deep learning, transformer architectures, graph neural networks, reinforcement learning, multimodal foundation models, and generative AI facilitate characterization of cellular communication, immune dynamics, stromal remodeling, angiogenesis, and spatial biological organization while supporting personalized diagnosis, prognostic prediction, immunotherapy optimization, adaptive therapeutics, and clinical decision support. Despite remarkable technological progress, challenges remain regarding data harmonization, computational complexity, explainability, interoperability, cybersecurity, regulatory validation, and ethical implementation. This review provides a comprehensive overview of computational tumor microenvironment modeling using artificial intelligence and digital twins, highlighting computational foundations, clinical applications, emerging innovations, and future perspectives for precision oncology.[1]