Accurate preoperative assessment of gastric cancer stage guides eligibility for endoscopic resection, extent of gastrectomy and lymphadenectomy, selection for neoadjuvant therapy, and use of staging laparoscopy. Contrast-enhanced CT (CECT) is guideline-endorsed for initial staging, yet performance varies across institutions and readers. This study will evaluate an artificial-intelligence (AI) system that analyzes routine CECT to detect gastric cancer and assign four-class T stage (T1-T4) and N stage (N0-N3) .

Adults with confirmed gastric cancer undergoing pre-treatment CECT will be enrolled. The AI analysis will be applied to clinically acquired images. Radiologist interpretations with and without AI support will be collected in a prespecified reader study. The reference standard will include surgical pathology, supplemented by clinical follow-up when applicable. The primary outcome is detection performance, diagnostic performance of the AI for four-class staging (e.g., accuracy and area under the receiver operating characteristic curve). Secondary outcomes include the effect of AI assistance on reader accuracy and interpretation time, inter-reader agreement, and cross-site reproducibility.

Inclusion Criteria:

1. pathologically confirmed gastric cancer;
2. preoperative contrast-enhanced CT performed;
3. no evidence of distant metastasis on baseline staging;
4. curative-intent management with complete postoperative histopathology.

Exclusion Criteria:

1. prior treatment before surgery;
2. non-diagnostic or poor-quality CT precluding evaluation.

This study will enroll adult patients (≥18 years old) with a confirmed diagnosis of gastric cancer (adenocarcinoma) who have undergone contrast-enhanced CT (CECT) as part of their standard preoperative evaluation. Participants will be selected from both internal and external cohorts, with inclusion from multiple centers to assess cross-site reproducibility and generalizability of the AI model.