Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-01-12

Study Completion Date

2025-12-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Study of Predicting Lymph Node Metastasis of High-risk Prostate Cancer by Artificial Intelligence Multi-omics Analysis

NCT07112599

Prostate Cancer Lymph Node Cancer Metastatic Artificial Intelligence (AI)

RECRUITING

To Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence

NCT06360523

Prostate Cancer

RECRUITING NA

Artificial Intelligence (AI)-Assisted Risk-based Prostate Cancer Detection

NCT05443412

Prostate Cancer

RECRUITING

Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

NCT06662708

Prostate Cancer Prostate Intraductal Carcinoma Prostate Cancer Aggressiveness +2 more

NOT_YET_RECRUITING NA

Artificial Intelligence-Based Computer-Aided Diagnosis of Prostate Cancer

NCT05513638

the Application of Artificial Intelligence in the Diagnosis of Prostate Cancer

RECRUITING

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Prostatic Neoplasms Lymphatic Metastasis

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Patients undergoing PLND

Patients (will) undergo radical prostatectomy and pelvic lymph node dissection

Artificial intelligence (AI)-based diagnostic model (developed)

Intervention Type DIAGNOSTIC_TEST

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.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Patients with prostate cancer, undergoing radical prostatectomy and pelvic lymph node dissection.
* Patients with complete clinical and pathological information.