The First Affiliated Hospital of Nanjing Medical University
Nanjing, Jiangsu, China
Location status: Recruiting
NCT Number: NCT07250347
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) .
Interested in participating?
Request Info18 year–85 year
All sexes
Observational
Nanjing, Jiangsu, China
Location status: Recruiting
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
preoperative contrast-enhanced CT
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.
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.
Time frame: 3 years
Calculate the survival time of gastric cancer patients from the point of diagnosis and treatment initiation.
Contact information is provided by the study sponsor or research team.
Qiong Li
CONTACT
Zhang Yudong, PHD, MD
CONTACT
The First Affiliated Hospital with Nanjing Medical University
Other
Langue and Imaging-integrated Foundation Model for Gastric Cancer Detection and Staging Via Contrast-Enhanced CT: a Multicenter Study
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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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