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NCT Number: NCT07250347

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

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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Key information

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Nanjing Medical University

Nanjing, Jiangsu, China

Location status: Recruiting

Location contact

Yue Wang

CONTACT

[email protected]

025-68306222

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

CT Scan

Diagnostic Test

preoperative contrast-enhanced CT

Primary outcomes

  1. 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.

Secondary outcomes

  1. 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.

  2. Survival time

    Time frame: 3 years

    Calculate the survival time of gastric cancer patients from the point of diagnosis and treatment initiation.

Study contacts

Contact information is provided by the study sponsor or research team.

Qiong Li

CONTACT

[email protected]

+8618351977281

Zhang Yudong, PHD, MD

CONTACT

[email protected]

+8618251966069

Sponsors and collaborators

Lead sponsor

The First Affiliated Hospital with Nanjing Medical University

Other

Collaborators

  • Jiangsu Cancer Institute & Hospital
  • Peking University First Hospital
  • Zhengzhou University

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2028
Study completion
2028
First posted
Nov 26, 2025
Registry last updated
Nov 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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