Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.

ABSTRACT: Current approaches to estimating cell trajectories, tumor progression dynamics, and cell population diversity of tumor microenvironment often depend on single-cell RNA sequencing, which is costly and resource intensive. To address this limitation, we developed an artificial intelligence (AI) model that leverages cell morphology features and histological spatial organization to classify tumor cell differentiation status, infer cell dynamic trajectories, and quantify tumor progression from hematoxylin and eosin (H&E)-stained whole-slide images. In three independent lung adenocarcinoma cohorts, our AI-based model accurately predicted cell differential status and provided quantifiable measures of tumor progression that were prognostic of patient survival. Spatial transcriptomic integrative ana

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12273761 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature