Exploring the potential of artificial intelligence in assessing the risk of gastric neoplastic lesions in patients with corpus atrophic gastritis.

ABSTRACT:

Background

Corpus atrophic gastritis (CAG) requires endoscopic-histological surveillance due to the risk of developing gastric neoplastic lesions (GNL). This study aimed to identify variables associated with GNL development at long-term follow-up using a Fisher score-based feature-ranking-approach coupled with a One-Class Support-Vector-Machine (SVM) model.

Methods

A dataset containing 30 clinical, endoscopic, and histological variables from consecutive CAG patients (2001-2023) adhering to a surveillance-program was considered. GNL presence at the longest available follow-up was recorded. Gastric biopsies and histological evaluations followed the updated-Sydney-system. A Fisher score-based feature ranking method and a One-Class SVM were employed to select key variables linke

SUBMITTER: Dilaghi E 

PROVIDER: S-EPMC12830466 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature