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

Condition(s) studied

Study 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.

Eligibility

Primary outcome measure(s)

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

Trial sites (1)

More Nanfang Hospital, Southern Medical University trials in China

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