This study aims to develop a model for predicting postoperative recurrence in patients with LAGC using artificial intelligence (AI) technology based on preoperative computed tomography (CT) images
Eligibility
Sex
ALL
Min age
18 Years
Max age
85 Years
Healthy volunteers
No
Inclusion Criteria:
1. pathology diagnosis of LAGC (pT2NxM0-pT4NxM0);
2. radical gastrectomy with D2 lymph node dissection (\>15 lymph nodes);
3. available clinicopathological data;
4. patients underwent contrast-enhanced abdominal CT scans within 4 weeks before surgery.
Exclusion Criteria:
1. preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy);
2. previous malignancies;
3. unsatisfactory gastric distention or inability to identify the primary tumor;
4. image artifacts.
Primary outcome measure(s)
Accuracy of early recurrence models — Immediately evaluated after the early recurrence model was built In this study, clinical data and contrast-enhanced CT imaging data of 550 patients with locally advanced gastric cancer from our hospital were collected. Machine learning and deep learning algorithms were applied to assess the early recurrence of patients within one year after surgery. The performance of the artificial intelligence model was evaluated from two dimensions: diagnostic accuracy and stability, and quantitative analysis of its performance was conducted using indicators including the area under the curve (AUC) and the precision-recall curve (PR curve).
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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