An observational study in Prostatic Neoplasms and Lymphatic Metastasis, sponsored by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University. Completed at 1 site in China. Open to male participants. Per ClinicalTrials.gov, last updated 2026-02-11.
Sponsored by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Observational
The goal of this diagnostic test is to prospectively test the performance of pre-developed artificial intelligence (AI) diagnostic model for detecting pathological lymph node metastasis (LNM) of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.
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 LNM in prostate cancer in the real world.
Lymph node metastasis (LNM) is a common mode of metastasis in prostate cancer, 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 developed an AI diagnostic model for detecting pathological lymph node metastasis of prostate cancer based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.
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.
6,370 studies on the registry are indexed under Prostatic Neoplasms; 1,400 are open to participants now.
This study's enrollment of 225 is above the median of 200 across 1,181 observational studies indexed under Prostatic Neoplasms.
Browse Prostatic Neoplasms studies →Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University is the lead sponsor of 466 studies on the registry; 271 are open to participants now.
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Patients with prostate cancer, (will) undergo radical prostatectomy and pelvic lymph node dissection between Jan, 2024 and Dec 2025 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University are planned to be enrolled in this prospective diagnostic test. Histopathological slides of resected pelvic lymph nodes of enrolled patients will be collected and digitised as whole-slide images (WSIs) for prospective validation of the AI model.
Exclusion Criteria:
Patients (will) undergo radical prostatectomy and pelvic lymph node dissection
Diagnostic Test: Artificial intelligence (AI)-based diagnostic model (developed)
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.
sensitivity
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 not long after pelvic lymph node dissection, and the sensitivity of the AI model will be evaluated through study completion, an average of 2 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 not long after pelvic lymph node dissection, and the specificity of the AI model will be evaluated through study completion, an average of 2 year.
Documents are hosted by the registry — open the source record to download them.
Plan to share: No — To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.
This study is completed, as verified in Feb 2026. You cannot join it, but the record below documents what was studied.
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University