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Enrolling by Invitation

NCT Number: NCT07683195

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

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

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Key information

Conditions

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

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

QianfoshanH

Jinan, Shandong, 250014, China

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

  • 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.

Exclusion criteria

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

Treatment and study plan

Primary outcomes

  1. Accuracy of early recurrence models

    Time frame: 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).

Sponsors and collaborators

Lead sponsor

Liu Yang

Other

Registry information

Acronym: LAGC

Important dates

Study start
2020
Primary completion
2024
Study completion
2026
First posted
Jul 6, 2026
Registry last updated
Jul 7, 2026

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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