Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.

ABSTRACT:

Background

Current cancer staging methods cannot accurately predict survival outcomes and therapeutic benefits in cancer patients. Digital pathomics, a rapidly evolving field, holds significant potential to revolutionize disease evaluation.

Methods

This international multicenter study included 2463 patients with pan-gastrointestinal cancer from twelve cohorts of seven cancer centers, of whom 1653 patients were diagnosed with gastric cancer (GC). We proposed a deep learning pathomics signature (DLPS) by integrating information on three scales from the whole slide images (WSIs) of H&E-stained tissue, including pathomic features related to the nucleus, microenvironment, and single-cell spatial distribution. Next, we assessed the predictive accuracy of the DLPS for prognosis, ch

SUBMITTER: Zhang T 

PROVIDER: S-EPMC12522295 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature