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.
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.
Study Type
OBSERVATIONAL
Enrollment
10,000
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.
Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
RECRUITINGsensitivity
the number of correctly diagnosed positive slides (with lymphatic metastasis), to be divided by the number of positive slides in total
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.
specificity
the number of correctly diagnosed negative slides (without lymphatic metastasis), to be divided by the number of negative slides in total
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.
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