Lymphatic Metastasis, Prostatic Neoplasms
Conditions
Keywords
artificial intelligence, lymph node metastasis, prostate cancer, whole slide image
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTArtificial 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.
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
MALE
Healthy volunteers
No
Inclusion criteria
* Patients with prostate cancer, undergoing radical prostatectomy and pelvic lymph node dissection. * Patients with complete clinical and pathological information.
Exclusion criteria
* Patients with other tumors that metastasized to pelvic lymph nodes. * The patient refused to participate in this diagnostic test.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| sensitivity | 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. | the number of correctly diagnosed positive slides (with lymphatic metastasis), to be divided by the number of positive slides in total |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| specificity | 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. | the number of correctly diagnosed negative slides (without lymphatic metastasis), to be divided by the number of negative slides in total |
Countries
China
Contacts
STUDY_CHAIRTianxin Lin, Ph.D
Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Outcome results
None listed