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.
Patients with cancer, undergoing radical tumor resection and lymph node dissection.
Patients with complete clinical and pathological information.
The patient refused to participate in this diagnostic test.
Patients with cancer, undergo radical tumor resection and lymph node dissection are planned to be enrolled in this diagnostic test. Histopathological slides of resected pelvic lymph nodes of enrolled patients will be collected and digitised as whole-slide images (WSIs) for the validation of the AI model.
Cohort
Prospective
Artificial intelligence (AI)-based diagnostic model
Diagnostic testCollect 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.
sensitivity
the number of correctly diagnosed positive slides (with lymphatic metastasis), to be divided by the number of positive slides in total
specificity
the number of correctly diagnosed negative slides (without lymphatic metastasis), to be divided by the number of negative slides in total
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