DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.

ABSTRACT:

Background

Glycolysis proved to have a prognostic value in lung cancer; however, to identify glycolysis-related genomic markers is expensive and challenging. This study aimed at identifying glycolysis-related computed tomography (CT) radiomics features to develop a deep-learning prognostic model for non-small cell lung cancer (NSCLC).

Methods

The study included 274 NSCLC patients from cohorts of The Second Affiliated Hospital of Soochow University (SZ; n=64), the Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), and the Gene Expression Omnibus dataset (n=136). Initially, the glycolysis enrichment scores were evaluated using a single-sample gene set enrichment analysis, and the cut-off values were optimized to investigate the prognostic potential of glycolysis genes. Radiomic

SUBMITTER: Fu T 

PROVIDER: S-EPMC11535831 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature