Development and Application of an Artificial Intelligence-driven Accurate Identification Model for Gastric Cancer Lymph Node Metastasis
NCT ID: NCT06534814
Last Updated: 2024-08-02
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
300 participants
OBSERVATIONAL
2024-07-01
2030-07-30
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
2. Lymph Node Involvement: Suspected or confirmed involvement of lymph nodes, as indicated by imaging studies or pathology reports.
3. Age: Patients aged 18 years or older.
4. Performance Status: An Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, indicating a functional status that allows participation in the study.
5. Informed Consent: Ability to provide written informed consent to participate in the study.
Exclusion Criteria
2. Severe Comorbid Conditions: Presence of severe comorbid medical conditions that could interfere with the study or pose additional risks.
3. Previous AI-Driven Diagnostic Intervention: Prior use of any AI-driven diagnostic models specifically for gastric cancer lymph node metastasis.
4. Inability to Comply: Inability or unwillingness to comply with study procedures, including follow-up visits and data collection.
5. Mental or Cognitive Impairment: Conditions that impair the ability to provide informed consent or participate effectively in the study.
18 Years
ALL
No
Sponsors
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Hebei Medical University
OTHER
Responsible Party
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Qun Zhao
Professor
Locations
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Department of General Surgery
Shijiazhuang, Hebei, China
Countries
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Facility Contacts
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Other Identifiers
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FUTURE08
Identifier Type: -
Identifier Source: org_study_id
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