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Recruiting NCT06534814

Development and Application of an Artificial Intelligence-driven Accurate Identification Model for Gastric Cancer Lymph Node Metastasis

— Observational The Primary Focus of This Study is on Gastric Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: AI-Driven Identification Model for Gastric Cancer Lymph Node Metastasis (AID-GLNM).
Who it may be relevant to
Registry conditions: The Primary Focus of This Study is on Gastric Cancer. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

Hebei Medical University

Overview

The clinical trial titled "Development and Application of an Artificial Intelligence-Driven Accurate Identification Model for Gastric Cancer Lymph Node Metastasis" aims to enhance the detection and treatment of gastric cancer through the utilization of cutting-edge artificial intelligence (AI) technology. This study will develop an AI-driven model designed to accurately identify lymph node metastasis in patients with gastric cancer, which is crucial for staging the disease and planning effective treatment strategies. The trial will involve a multidisciplinary team of oncologists, radiologists, data scientists, and AI experts who will collaborate to create a robust and precise identification system. Participants will undergo standard diagnostic procedures, and the AI model will analyze imaging and pathological data to predict lymph node involvement. By comparing the AI model's predictions with traditional diagnostic methods, the study seeks to validate the model's accuracy and efficiency. This approach is expected to improve early detection rates, reduce diagnostic errors, and ultimately lead to better clinical outcomes for patients with gastric cancer. The successful implementation of this AI-driven model could revolutionize the current standards of care and serve as a blueprint for integrating AI technologies in other cancer diagnoses and treatments.

Interventions

  • Diagnostic test AI-Driven Identification Model for Gastric Cancer Lymph Node Metastasis (AID-GLNM)
    The AI-Driven Identification Model for Gastric Cancer Lymph Node Metastasis (AID-GLNM) intervention involves the development and application of an advanced artificial intelligence (AI) system specifically designed to enhance the identification and characterization of lymph node metastasis in patients diagnosed with gastric cancer.

Primary outcome measures

  • Identification of metastatic lymph nodes [Time frame: 2025-12-31]

Eligibility criteria

Inclusion criteria

  • Diagnosis of Gastric Cancer: Confirmed diagnosis of gastric cancer, either newly diagnosed or recurrent.
  • Lymph Node Involvement: Suspected or confirmed involvement of lymph nodes, as indicated by imaging studies or pathology reports.
  • Age: Patients aged 18 years or older.
  • Performance Status: An Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, indicating a functional status that allows participation in the study.
  • Informed Consent: Ability to provide written informed consent to participate in the study.

Exclusion criteria

  • Pregnancy or Lactation: Pregnant or lactating women, due to potential risks to the fetus or infant.
  • Severe Comorbid Conditions: Presence of severe comorbid medical conditions that could interfere with the study or pose additional risks.
  • Previous AI-Driven Diagnostic Intervention: Prior use of any AI-driven diagnostic models specifically for gastric cancer lymph node metastasis.
  • Inability to Comply: Inability or unwillingness to comply with study procedures, including follow-up visits and data collection.
  • Mental or Cognitive Impairment: Conditions that impair the ability to provide informed consent or participate effectively in the study.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Department of General Surgery — Shijiazhuang

Identifiers

NCT: NCT06534814 · FUTURE08

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗