AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging

Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging. This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support. The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.

Sex

All

Minimum age

18 Years

Maximum age

80 Years

Inclusion Criteria:

* Age ≥18 years
* Suspicious gastric lesions identified on white-light imaging (WLI)
* Preoperative biopsy indicating precancerous lesions (dysplasia or intraepithelial neoplasia) or adenocarcinoma, with preoperative magnifying endoscopy with narrow-band imaging (ME-NBI) performed
* Patients meeting the absolute indications for endoscopic submucosal dissection (ESD) and who underwent ESD

Exclusion Criteria:

* Non-adenocarcinoma histological types (e.g., lymphoma)
* Patients who did not undergo ME-NBI examination or did not receive ESD
* Lesions invading the muscularis propria or deeper layers
* Missing or indeterminate postoperative histopathological results