Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data.

ABSTRACT: Breast cancer is a highly heterogeneous disease with diverse outcomes, and intra-tumoral heterogeneity plays a significant role in both diagnosis and treatment. Despite its importance, the spatial distribution of intra-tumoral heterogeneity is not fully elucidated. Spatial transcriptomics has emerged as a promising tool to study the molecular mechanisms behind many diseases. It offers accurate measurements of RNA abundance, providing powerful tools to correlate the morphologies of cellular neighborhoods with localized gene expression patterns. However, the spot-based spatial transcriptomic tools, including the most widely used platform, Visium, do not achieve single-cell resolution readouts, which hinders data interpretability. In this study, we present a computational pathology image anal

SUBMITTER: Li T 

PROVIDER: S-EPMC12420830 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature