Research on Early Recurrence of Locally Advanced Gastric Cancer Based on CT Radiomics Prediction

NCT07683195 | Enrolling By Invitation DMC

• 1 other identifier: YXYXLL-KY-2025[168]
• Study Type: observational
• Target: 900
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Gastric Cancer

Keywords

Locally Advanced Gastric Cancer; Early Recurrence; CT; Radiomics

Outcome Measures

Primary Outcomes (1)

• Accuracy of early recurrence models

  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).

  Immediately evaluated after the early recurrence model was built

Study Arms (2)

No recurrence

Patients with locally advanced gastric cancer who have experienced no recurrence within 1 year after radical gastrectomy

Recurrence

Patients with locally advanced gastric cancer who experienced recurrence within 1 year after radical gastrectomy

Eligibility Criteria

• Age: 18 Years - 85 Years
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients with locally advanced gastric cancer

You may qualify if:

• pathology diagnosis of LAGC (pT2NxM0-pT4NxM0);
• radical gastrectomy with D2 lymph node dissection (\>15 lymph nodes);
• available clinicopathological data;
• patients underwent contrast-enhanced abdominal CT scans within 4 weeks before surgery.

You may not qualify if:

• preoperative treatment for LAGC (radiotherapy, chemotherapy, or systemic therapy);
• previous malignancies;
• unsatisfactory gastric distention or inability to identify the primary tumor;
• image artifacts.

Sponsors & Collaborators

• Liu Yang: lead

Study Sites (1)

QianfoshanH

Jinan, Shandong, 250014, China

Location

MeSH Terms

Conditions

Stomach Neoplasms; Recurrence

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Guangyong Zhang

  Qianfoshan Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Chief Physician

Study Record Dates

• First Submitted: June 26, 2026
• First Posted: July 6, 2026
• Study Start: January 1, 2020
• Primary Completion: December 31, 2024
• Study Completion (Estimated): August 31, 2026
• Last Updated: July 7, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)