Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
NCT ID: NCT06517979
Last Updated: 2025-11-28
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
10000 participants
OBSERVATIONAL
2024-07-26
2027-06-30
Brief Summary
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Investigators will compare the diagnostic performance (sensitivity, specificity, etc.) of the AI model and routine pathological report issued by pathologists, to see if the AI model can improve the clinical workflow of pathological evaluation of cancer LNM in in the real world.
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Detailed Description
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Therefore, investigators are to develope an artificial intelligence (AI)-based diagnostic model for detecting pathological cancer lymph node metastasis based on deep learning algorithms, and evaluate its apllication value in the real-world clinical settings.
This study is a diagnostic test with no intervention measures, planning to collect pathological slides of formalin-fixed, paraffin-embedded lymph nodes resected from the enrolled patients and digitise them into whole-slide images (WSIs). The AI model will analyse the WSIs and generate pixel-level heatmaps and slide-level diagnostic results (with or without LNM). The routine pathological examination will be performed as usual. These two processes will not interfere with each other. And if there are inconsistency in slide-level classification between AI and routine pathological examination, investigators would convene senior pathologists for discussion to make the final decision (immunohistochemistry would be performed if necessary). The final result will be presented to the patient in the form of a pathological report.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Patients with cancer undergoing LND
Patients undergo radical tumor resection and lymph node dissection (LND)
Artificial intelligence (AI)-based diagnostic model
Collect pathological slides of resected lymph nodes of the enrolled patients. Digitise these slides into whole-slide images (WSIs). Analyze the WSIs using the AI model to generate diagnostic results (with or without lymphatic metastasis). No intervention to patients would be performed in this diagnostic test study.
Interventions
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Artificial intelligence (AI)-based diagnostic model
Collect pathological slides of resected lymph nodes of the enrolled patients. Digitise these slides into whole-slide images (WSIs). Analyze the WSIs using the AI model to generate diagnostic results (with or without lymphatic metastasis). No intervention to patients would be performed in this diagnostic test study.
Eligibility Criteria
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Inclusion Criteria
* Patients with complete clinical and pathological information.
Exclusion Criteria
ALL
No
Sponsors
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Locations
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Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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SYSKY-2024-513-01
Identifier Type: -
Identifier Source: org_study_id
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