Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis

NCT06517979 | Recruiting

• 1 other identifier: SYSKY-2024-513-01
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this diagnostic test is to develop an artificial intelligence (AI)-based pan-cancer universal diagnostic model for detecting pathological lymph node metastasis (LNM), and prospectively evaluate its apllication value in the real-world clinical practice. 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 cancer LNM in in the real world.

Conditions

Cancer; Lymphatic Metastasis

Keywords

Artificial Intelligence; Lymph Node Metastasis; Digital Pathology; 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 servel days after lymph node dissection, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.

Secondary Outcomes (1)

• specificity

  For each enrolled patient, the diagnosis results of AI model will be obtained in servel days after lymph node dissection, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.

Study Arms (1)

Patients with cancer undergoing LND

Patients undergo radical tumor resection and lymph node dissection (LND)

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

Interventions

Artificial intelligence (AI)-based diagnostic model 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 with cancer undergoing LND

Eligibility Criteria

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

Study Population

Patients with cancer, undergo radical tumor resection and lymph node dissection are planned to be enrolled in this diagnostic test. Histopathological slides of resected pelvic lymph nodes of enrolled patients will be collected and digitised as whole-slide images (WSIs) for the validation of the AI model.

You may qualify if:

• Patients with cancer, undergoing radical tumor resection and lymph node dissection.
• Patients with complete clinical and pathological information.

You may not qualify if:

• 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

RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

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

MeSH Terms

Conditions

Neoplasms; Lymphatic Metastasis

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Neoplasm Metastasis; Neoplastic Processes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Central Study Contacts

Lin Tianxin, Ph.D

CONTACT

13724008338, China; lintx@mail.sysu.edu.cn

Wu Shaoxu, MD

CONTACT

15017581087, China; wushx29@mail.sysu.edu.cn

Study Design

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

Study Record Dates

• First Submitted: July 18, 2024
• First Posted: July 24, 2024
• Study Start: July 26, 2024
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): June 30, 2027
• Last Updated: November 28, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)