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

NCT07250347 | Recruiting DMC

• 1 other identifier: 2025-SR-842
• Study Type: observational
• Target: 8,000
• Locations: 1 country
• Sites: 1

Brief 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) .

Conditions

Gastric Cancer Stage; Gastric Cancer Patients Undergoing Gastrectomy

Keywords

Gastric cancer; stage; artificial-intelligence; detection; contrast-enhanced CT

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of the AI model for staging

  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.

  3 years

Secondary Outcomes (2)

• Reader Accuracy with AI Support

  3 years

• Survival time

  3 years

Study Arms (3)

Cohort 1 (Internal Derivation Cohort)

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.

Diagnostic Test: CT scan

Cohort 2 (External Validation Cohort A)

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.

Diagnostic Test: CT scan

Cohort 3 (External Validation Cohort B)

A second independent retrospective validation cohort from another institution to further test generalizability.

Diagnostic Test: CT scan

Interventions

CT scan DIAGNOSTIC_TEST

preoperative contrast-enhanced CT

Cohort 1 (Internal Derivation Cohort); Cohort 2 (External Validation Cohort A); Cohort 3 (External Validation Cohort B)

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• The First Affiliated Hospital with Nanjing Medical University: lead
• Jiangsu Cancer Institute & Hospital: collaborator
• The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital: collaborator
• Peking University First Hospital: collaborator

Study Sites (1)

The First Affiliated Hospital of Nanjing Medical University

Nanjing, Jiangsu, China

RECRUITING

MeSH Terms

Conditions

Stomach Neoplasms

Interventions

Tomography, X-Ray Computed

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Intervention Hierarchy (Ancestors)

Image Interpretation, Computer-Assisted; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Radiographic Image Enhancement; Image Enhancement; Photography; Radiography; Tomography, X-Ray; Tomography

Study Officials

• Zhang Yudong

  The First Affiliated Hospital with Nanjing Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Zhang Yudong, PHD, MD

CONTACT

+8618251966069; zhangyd3895@njmu.edu.cn

Qiong Li

CONTACT

+8618351977281; njmu_lq@163.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 29, 2025
• First Posted: November 26, 2025
• Study Start: August 1, 2025
• Primary Completion (Estimated): December 30, 2028
• Study Completion (Estimated): December 30, 2028
• Last Updated: November 26, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)