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

November 23, 2025 updated by: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Lymph node metastasis (LNM) is a common mode of cancer metastasis, 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 are to develope an artificial intelligence (AI)-based diagnostic model for detecting pathological cancer lymph node metastasis based on deep learning algorithms, and evaluate its apllication value in the real-world clinical settings.

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.

Study Type

Observational

Enrollment (Estimated)

10000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510120
        • Recruiting
        • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

  • The patient refused to participate in this diagnostic test.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients with cancer undergoing LND
Patients undergo radical tumor resection and lymph node dissection (LND)
Diagnostic test: Artificial intelligence (AI)-based diagnostic model
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
sensitivity
Time Frame: 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.
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
specificity
Time Frame: 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.
the number of correctly diagnosed negative slides (without lymphatic metastasis), to be divided by the number of negative 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.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 26, 2024

Primary Completion (Estimated)

June 30, 2027

Study Completion (Estimated)

June 30, 2027

Study Registration Dates

First Submitted

July 18, 2024

First Submitted That Met QC Criteria

July 18, 2024

First Posted (Actual)

July 24, 2024

Study Record Updates

Last Update Posted (Actual)

November 28, 2025

Last Update Submitted That Met QC Criteria

November 23, 2025

Last Verified

November 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SYSKY-2024-513-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

To protect patient privacy, pathological slide images and other patient-related data are not publicly accessible.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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