Spatial Pathomics

Spatial pathomics integrates histological images with genomic and other multi-omics data to analyze tissue microenvironments and improve cancer diagnosis and prognosis. Current research focuses on developing machine learning models, particularly deep learning architectures like transformers and convolutional neural networks, to automatically segment tumors, extract quantitative features from whole slide images, and predict genetic mutations or patient survival outcomes. These advancements offer the potential for more precise and efficient cancer diagnostics and personalized treatment strategies by leveraging the spatial context of cellular and molecular information within tissue samples.