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

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 linked to GNL development, and then validated with synthetically generated data. Results: overall, 355 CAG patients were initially considered. Of these, 36 were excluded due to the presence of GNL at baseline gastroscopy, and 216 for missing data. Thus, a total of 103 patients were considered and grouped into: CAG patients with[22 patients (F 68.1%), median-age 68(35-83) years] and without GNL at follow-up[81 patients (F 72.8%) median-age 59(26-84) years]. After a median follow-up of 60(12-192) months, 13 epithelial GNL(gastric adenocarcinoma or high/low-grade dysplasia) and nine type-1 gastric-neuroendocrine-tumors(T1gNET) were recorded. Parietal-cell-antibodies and pepsinogen-I <30 μg/l were associated with epithelial GNL and T1gNET. Antral inflammation and age>60 were linked to epithelial GNL, while anti-thyroperoxidase-antibodies, smoking, and dyspeptic-symptoms were linked to T1gNET. Low-dose aspirin and H. pylori eradication therapy showed inverse associations with epithelial GNL and T1gNET, respectively. Conclusions: this is the first study in which an AI-model simultaneously considers clinical, endoscopic, and histological features from a dataset of CAG patients, showing the potential to identify variables associated with GNL development.