Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application

The tumor microenvironment (TME), composed of tumor cells together with stromal cells, immune cells, vascular networks, and other components, constitutes a complex ecosystem that plays a decisive role in tumor initiation, progression, metastasis and therapeutic response. Traditional pathological diagnosis mainly relies on pathologists manually examining H&E-stained tissue sections under the microscope, a method that not only suffers from substantial interobserver variability but also has relatively low analytical efficiency. With the rapid development of computational pathology, the integration of whole-slide imaging technology and deep learning algorithms has provided powerful tools for characterizing tumor microenvironment. These techniques enable automated characterization of cellular, spatial and molecular heterogeneity within the tumor microenvironment, providing integrated insights that advance precision diagnostics and improve prediction of therapeutic response and patient outcomes. Based on a comprehensive review of existing research, this paper highlights recent advances in deep learning-driven recognition of panoramic TME features from H&E slides and their clinical applications and further discusses both the translational potential and current limitations of this technology in oncology research and clinical applications.