Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions

The tumor microenvironment [TME] is a dynamic ecosystem where spatial organization and metabolic reprogramming play a crucial role in immune response, tumor progression, and therapeutic response. Recent breakthroughs in spatial transcriptomics, metabolomics, and multiplexed imaging studies have shown that complex immunometabolic niches are involved in therapeutic resistance, including conventional and immunotherapeutic approaches. Artificial intelligence [AI] technology has been recognized as a revolutionary concept that allows the integration of complex data, thereby facilitating the scalable extraction of spatial, molecular, and cellular features from routine histopathology and multi-omics platforms. This review of the current evidence on AI-based spatial immunometabolic studies of the tumor microenvironment aims to provide a comprehensive overview of the current evidence, including AI-based spatial immunometabolic studies of the tumor mi-croenvironment, with special reference to digital pathology, spatial transcriptomics, and multimodal data fusion. The current challenges, including data heterogeneity, model interpretability, generalizability, and biological validation, will be discussed. The emerging trends in AI-based spatial immunometabolism, including multimodal foundation models, federated learning, and spatially resolved target discovery, will be discussed. AI-based spatial immunometabolism will be a cornerstone in precision oncology, with the potential to improve patient stratification, therapeutic approaches, and clinical translation.