Automated Spatial Immunophenotyping of Tumor Microenvironment via Multi-Modal Fusion and Recursive Evaluation (SAMF-RE)

**Abstract:** Traditional single-cell mass cytometry (CyTOF) analysis offers unprecedented resolution of immune cell populations within tumors. However, existing methods largely overlook spatial context, limiting translational utility. This paper introduces SAMF-RE, an automated framework for spatial immunophenotyping of tumor microenvironments, fusing CyTOF data with multiplexed spatial transcriptomics (ST) and advanced machine learning algorithms for robust and accurate characterization of cell-cell interactions and tissue organization. SAMF-RE achieves a 10x improvement in spatial resolution and predictive accuracy compared to conventional CyTOF analysis by leveraging a recursive evaluation loop and a multi-layered assessment pipeline, unlocking new avenues for personalized cancer therapy development.