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

NCT07263711 | Recruiting

• 1 other identifier: NFEC-2025-508
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Glioma

Outcome Measures

Primary Outcomes (1)

• Accuracy of AI model in predicting key molecular alterations in glioma

  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

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Nanfang Hospital, Southern Medical University: lead

Study Sites (1)

Nanfang Hospital, Southern Medical University

Guangzhou, Guangdong, 510515, China

RECRUITING

MeSH Terms

Conditions

Glioma

Condition Hierarchy (Ancestors)

Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue

Study Officials

• LI LIANG

  Nanfang Hospital, Southern Medical University

  STUDY DIRECTOR

Central Study Contacts

DANYI LI

CONTACT

+8613538308634; lidanyi26@163.com

Study Design

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

Study Record Dates

• First Submitted: November 13, 2025
• First Posted: December 4, 2025
• Study Start: September 1, 2025
• Primary Completion (Estimated): September 1, 2030
• Study Completion (Estimated): September 1, 2030
• Last Updated: December 4, 2025

Record last verified: 2025-11

Locations

CN (1)