Introduction

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 feature ranking method based on a novel One-Class Support Vector Machine (SVM) model.

Aims & Methods

A dataset containing 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 were conducted according to the updated Sydney system. CAG patients with any missing data or a GNL diagnosis at the baseline gastroscopy were excluded from the final analysis. A Fisher score-based feature ranking method and One-Class SVM were used to select the optimal subset of baseline variables associated with GNL development, aiming to achieve average sensitivity (Se) and specificity (Sp) above 80%.

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 both epithelial GNL and T1gNET. Antral inflammation and age > 60 were linked to epithelial GNL, while anti-thyroperoxidase-antibodies, smoking, and dyspeptic symptoms at CAG diagnosis were linked to T1gNET. Low-dose aspirin and H. pylori eradication therapy showed inverse associations with epithelial GNL and T1gNET, respectively.

Conclusion

This is the first study in which an AI model simultaneously considers clinical, endoscopic, and histological characteristics from a dataset of CAG patients, showing good sensitivity and specificity in identifying those variables associated with developing GNL. Nonetheless, we acknowledge the need for improved variable standardization and data balancing between groups to enhance model performance further.