Department of General Surgery
Shijiazhuang, Hebei, 050011, China
Location status: Recruiting
Recruiting
NCT Number: NCT06534814
Development and Application of an Artificial Intelligence-driven Accurate Identification Model for Gastric Cancer Lymph Node Metastasis
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.
Recruiting
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Age range
18 year and older
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Only the study team can determine whether someone qualifies for participation.
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.
Treatment and study plan
AI-Driven Identification Model for Gastric Cancer Lymph Node Metastasis (AID-GLNM)
Diagnostic TestThe 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 outcomes
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Identification of metastatic lymph nodes
Time frame: 2025-12-31
A prediction model based on artificial intelligence technology was constructed to accurately identify metastatic perigastric lymph nodes before surgery.
Sponsors and collaborators
Lead sponsor
Hebei Medical University
Other
Registry information
Official study title
Hebei Medical University
Important dates
- Study start
- 2024
- Primary completion
- 2025
- Study completion
- 2030
- First posted
- Aug 2, 2024
- Registry last updated
- Aug 2, 2024
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.