ARTIFICIAL INTELLIGENCE–ENABLED TUMOR MICROENVIRONMENT MODELING: INTEGRATING SPATIAL BIOLOGY, MULTI-OMICS, AND DIGITAL TWINS

Abstract

The tumor microenvironment (TME) has emerged as one of the most important determinants of cancer initiation, progression, metastasis, immune evasion, and therapeutic response. Rather than functioning as an isolated population of malignant cells, tumors exist within highly dynamic ecosystems comprising immune cells, stromal fibroblasts, endothelial cells, extracellular matrix components, vascular networks, signaling molecules, metabolites, and microbiota that collectively shape disease evolution. Conventional analytical approaches frequently investigate these biological components independently, limiting comprehensive understanding of tumor ecosystem dynamics. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, spatial computing, and generative AI, have enabled comprehensive computational modeling of the TME through integration of spatial biology, multi-omics, digital pathology, radiological imaging, laboratory biomarkers, liquid biopsy, electronic health records, and longitudinal clinical data. These intelligent systems support biomarker discovery, molecular characterization, prognostic prediction, immunotherapy optimization, adaptive treatment planning, digital twin simulation, and intelligent clinical decision support while advancing systems oncology and precision medicine. Emerging innovations including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational modeling of tumor ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-enabled tumor microenvironment modeling, emphasizing integration of spatial biology, multi-omics, and digital twins as a transformative paradigm for personalized oncology.