Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images.

ABSTRACT: Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concord

SUBMITTER: Wang S 

PROVIDER: S-EPMC10713592 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature