Prospective Real-World Study of Pathology AI for Glioma Molecular Prediction

November 23, 2025 updated by: Nanfang Hospital, Southern Medical University

A Prospective Real-World Study of Pathology Artificial Intelligence for Predicting Molecular Alterations in Gliomas

The goal of this clinical study is to learn if an artificial intelligence (AI) model can accurately predict important molecular changes in gliomas, a type of brain tumor, using digital pathology images.

The main questions this study aims to answer are:

How accurate is the AI model in predicting key molecular alterations compared with standard molecular testing? Can the AI model shorten the time needed for diagnosis and reduce the need for expensive molecular tests?

Researchers will collect whole slide images from multiple hospitals and use the AI model to predict molecular results. The predictions will be compared with the actual test results from standard laboratory methods.

Participants will:

Allow the use of their pathology images and molecular test results for research.

Have no additional treatments or procedures beyond standard medical care.

This study will help determine whether AI-assisted tools can provide faster and lower-cost molecular diagnosis for glioma, improving patient care and supporting equal access to precision medicine.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

2000

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 Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510515
        • Recruiting
        • Nanfang Hospital, Southern Medical University
        • Contact:
        • 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

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

This study will include adult participants (aged 18 years or older) who have been diagnosed with or are suspected to have diffuse glioma based on biopsy or surgical resection. Participants must have available hematoxylin and eosin (H&E)-stained digital pathology slides and complete clinical and molecular testing data.

All participants will be patients who have received standard medical care for glioma. No additional treatments or interventions will be performed as part of this study. Pathology images and molecular testing results will be collected prospectively from multiple clinical centers to evaluate the diagnostic performance of the AI-based pathology model.

Description

Inclusion Criteria:

  • Participant (or legally authorized representative) has voluntarily signed the informed consent form.
  • Age ≥ 18 years at the time of enrollment.
  • Histologically suspected diffuse glioma based on biopsy or surgical resection.
  • Availability of complete clinical information and usable digital pathology slides with hematoxylin and eosin (H&E) staining.
  • Postoperative molecular pathology results available for comparison.

Exclusion Criteria:

  • Poor-quality pathology samples (e.g., insufficient tissue, large folding or contamination of slides, or substandard digital scanning quality).
  • Determined by the investigator to be unsuitable for participation in the study for any reason.

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of AI model in predicting key molecular alterations in glioma
Time Frame: Within 1 week after whole slide images (WSIs) are obtained
The primary outcome is the diagnostic performance of the AI-based pathology model in predicting key molecular alterations in glioma. Accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) will be calculated by comparing AI predictions with reference results from standard molecular pathology testing.
Within 1 week after whole slide images (WSIs) are obtained

Collaborators and Investigators

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

Sponsor

Nanfang Hospital, Southern Medical University

Investigators

  • Study Director: Li Liang, Nanfang Hospital, Southern Medical 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)

September 1, 2025

Primary Completion (Estimated)

September 1, 2030

Study Completion (Estimated)

September 1, 2030

Study Registration Dates

First Submitted

November 13, 2025

First Submitted That Met QC Criteria

November 23, 2025

First Posted (Estimated)

December 4, 2025

Study Record Updates

Last Update Posted (Estimated)

December 4, 2025

Last Update Submitted That Met QC Criteria

November 23, 2025

Last Verified

November 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • NFEC-2025-508

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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