Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
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: Artificial intelligence (AI)-based diagnostic model.
- Who it may be relevant to
- Registry conditions: Cancer, Lymphatic Metastasis. Basic parameters: No limits · 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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Overview
The goal of this diagnostic test is to develop an artificial intelligence (AI)-based pan-cancer universal diagnostic model for detecting pathological lymph node metastasis (LNM), and prospectively evaluate its apllication value in the real-world clinical practice. 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.
Detailed description
Lymph node metastasis (LNM) is a common mode of cancer metastasis, and accurate postoperative pathological lymph node staging is of great significance for further treatment and prognosis assessment. However, the current pathological evaluation of lymph nodes relies on manual examination by pathologists, which has a relatively low diagnostic efficiency and is prone to missed-diagnosis for micro metastatic lesions.
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.
Interventions
- Diagnostic test 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.
Primary outcome measures
- sensitivity [Time frame: For each enrolled patient, the diagnosis results of AI model will be obtained in servel days after lymph node dissection, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.]
Secondary outcome measures (1)
- specificity [Time frame: For each enrolled patient, the diagnosis results of AI model will be obtained in servel days after lymph node dissection, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.]
Eligibility criteria
Inclusion criteria
- Patients with cancer, undergoing radical tumor resection and lymph node dissection.
- Patients with complete clinical and pathological information.
Exclusion criteria
- The patient refused to participate in this diagnostic test.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Sun Yat-sen Memorial Hospital of Sun Yat-sen University — Guangzhou
Identifiers
NCT: NCT06517979 · SYSKY-2024-513-01