Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
概览
- 阶段
- 不适用
- 干预措施
- Artificial intelligence (AI)-based diagnostic model (developed)
- 疾病 / 适应症
- Prostatic Neoplasms
- 发起方
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- 入组人数
- 225
- 试验地点
- 1
- 主要终点
- sensitivity
- 状态
- 已完成
- 最后更新
- 2个月前
概览
简要总结
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.
详细描述
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.
研究者
入排标准
入选标准
- •Patients with prostate cancer, undergoing radical prostatectomy and pelvic lymph node dissection.
- •Patients with complete clinical and pathological information.
排除标准
- •Patients with other tumors that metastasized to pelvic lymph nodes.
- •The patient refused to participate in this diagnostic test.
研究组 & 干预措施
Patients undergoing PLND
Patients (will) undergo radical prostatectomy and pelvic lymph node dissection
干预措施: Artificial intelligence (AI)-based diagnostic model (developed)
结局指标
主要结局
sensitivity
时间窗: 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
次要结局
- 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.)