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

November 24, 2025 updated by: The First Affiliated Hospital with Nanjing Medical University

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

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) .

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

8000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • China
    • Jiangsu
      • Nanjing, Jiangsu, China
        • Recruiting
        • The First Affiliated Hospital of Nanjing Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
preoperative contrast-enhanced CT
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
preoperative contrast-enhanced CT
Cohort 3 (External Validation Cohort B)
A second independent retrospective validation cohort from another institution to further test generalizability.
Diagnostic test: CT scan
preoperative contrast-enhanced CT

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic performance of the AI model for staging
Time Frame: 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.
3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Reader Accuracy with AI Support
Time Frame: 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.
3 years
Survival time
Time Frame: 3 years
Calculate the survival time of gastric cancer patients from the point of diagnosis and treatment initiation.
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The First Affiliated Hospital with Nanjing Medical University

Collaborators

Peking University First Hospital
Jiangsu Cancer Institute & Hospital
The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital

Investigators

  • Principal Investigator: Zhang Yudong, The First Affiliated Hospital with Nanjing Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2025

Primary Completion (Estimated)

December 30, 2028

Study Completion (Estimated)

December 30, 2028

Study Registration Dates

First Submitted

September 29, 2025

First Submitted That Met QC Criteria

November 24, 2025

First Posted (Actual)

November 26, 2025

Study Record Updates

Last Update Posted (Actual)

November 26, 2025

Last Update Submitted That Met QC Criteria

November 24, 2025

Last Verified

September 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2025-SR-842

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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