AI-Assisted Detection and Staging of Gastric Cancer Using Contrast-Enhanced CT

NCT07250347 — Recruiting, 8000 participants, The First Affiliated Hospital with Nanjing Medical University

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Langue and Imaging-integrated Foundation Model for Gastric Cancer Detection and Staging Via Contrast-Enhanced CT: a Multicenter Study

Registry	NCT07250347
Status	Recruiting
Study type	Observational
Enrollment	8000 (Estimated)
Sponsor	The First Affiliated Hospital with Nanjing Medical University (Other); collaborators: Jiangsu Cancer Institute & Hospital, Zhengzhou University, Peking University First Hospital
Conditions	Gastric Cancer Stage, Gastric Cancer Patients Undergoing Gastrectomy
Interventions	CT scan (Diagnostic test)
Dates	start 2025-08-01, primary completion 2028-12-30 (estimated), completion 2028-12-30 (estimated), first posted 2025-11-26, last updated 2025-11-26
Locations	1 site in China

-Summary

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) .

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+Detailed description

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.

+Arms and interventions

Arm	Interventions	Description
Cohort 1 (Internal Derivation Cohort)	Diagnostic Test: CT scan	Retrospective case-only cohort of adults with pathologically confirmed gastric cancer who underwent preoperative contrast-enhanced CT at the sponsoring institution. Existing CT images and clinical/pathology records will be used to train and test the AI model and to estimate diagnostic performance for T and N staging.
Cohort 2 (External Validation Cohort A)	Diagnostic Test: CT scan	Independent retrospective case-only cohort from an external hospital with the same inclusion/exclusion criteria. Used solely for external validation to assess reproducibility across sites and scanners.
Cohort 3 (External Validation Cohort B)	Diagnostic Test: CT scan	A second independent retrospective validation cohort from another institution to further test generalizability.

CT scan (Diagnostic test): preoperative contrast-enhanced CT

+Outcome measures

Primary outcomes

Diagnostic performance of the AI model for staging [3 years]: The primary outcome is the diagnostic accuracy of the AI system for four-class T staging (T1-T4) and N staging (N0-3) based on contrast-enhanced CT. The AI performance will be assessed using accuracy, area under the receiver operating characteristic curve (AUC), and micro-AUC for internal and external cohorts.

Secondary outcomes

Reader Accuracy with AI Support [3 years]: This outcome measures the accuracy of radiologists in classifying gastric cancer stagewhen aided by the AI system compared to manual classification without AI assistance. Accuracy will be compared between different radiologist experience levels.
Survival time [3 years]: Calculate the survival time of gastric cancer patients from the point of diagnosis and treatment initiation.

+Eligibility

Sex: All, ages: 18 Years to 85 Years

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.

+Locations (1)

Facility	City	Country	Status
The First Affiliated Hospital of Nanjing Medical University	Nanjing, Jiangsu	China	Recruiting

+Investigators

Zhang Yudong, Principal investigator, The First Affiliated Hospital with Nanjing Medical University