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

NCT06253065 | Completed

• 1 other identifier: SYSKY-2023-1281
• Study Type: observational
• Target: 225
• Locations: 1 country
• Sites: 1

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.

Conditions

Prostatic Neoplasms; Lymphatic Metastasis

Keywords

artificial intelligence; lymph node metastasis; prostate cancer; whole slide image

Outcome Measures

Primary Outcomes (1)

• sensitivity

  the number of correctly diagnosed positive slides (with lymphatic metastasis), to be divided by the number of positive slides in total

  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.

Secondary Outcomes (1)

• 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.

Study Arms (1)

Patients undergoing PLND

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

Diagnostic Test: Artificial intelligence (AI)-based diagnostic model (developed)

Interventions

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

Patients undergoing PLND

Eligibility Criteria

• Sex: male
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

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

You may not qualify if:

• Patients with other tumors that metastasized to pelvic lymph nodes.
• The patient refused to participate in this diagnostic test.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Sun Yat-sen Memorial Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510120, China

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Histopathological slides of formalin-fixed, paraffin-embedded lymph nodes resected from patients with prostate cancer undergoing radical prostatectomy and pelvic lymph node dissection.

MeSH Terms

Conditions

Prostatic Neoplasms; Lymphatic Metastasis

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases; Neoplasm Metastasis; Neoplastic Processes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Tianxin Lin, Ph.D

  Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 24, 2024
• First Posted: February 12, 2024
• Study Start: January 12, 2024
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: February 11, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)