Gastric Cancer Patients Undergoing Gastrectomy, Gastric Cancer Stage
Conditions
Keywords
Gastric cancer, stage, artificial-intelligence, detection, contrast-enhanced CT
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) .
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.
Interventions
DIAGNOSTIC_TESTCT scan
preoperative contrast-enhanced CT
Sponsors
Jiangsu Cancer Institute & Hospital
The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital
Peking University First Hospital
The First Affiliated Hospital with Nanjing Medical University
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Contacts
Primary ContactZhang Yudong, PHD, MD
Backup ContactQiong Li
Outcome results
None listed