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NCT Number: NCT07263711

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

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.

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Key information

Conditions

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

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. 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.

Study contacts

Contact information is provided by the study sponsor or research team.

DANYI LI

CONTACT

[email protected]

+8613538308634

Sponsors and collaborators

Lead sponsor

Nanfang Hospital, Southern Medical University

Other

Registry information

Official study title

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

Important dates

Study start
2025
Primary completion
2030
Study completion
2030
First posted
Dec 4, 2025
Registry last updated
Dec 4, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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