Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Glioma
- Design
- Observational Model: CohortTime Perspective: Other
Participation Requirements
- Age
- Between 1 years and 95 years
- Gender
- Both males and females
Description
Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations is challenging. With the development of artificial intelligence, much more potential lies in the histopathology images of HE slices in primary gliomas could be excavated...
Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations is challenging. With the development of artificial intelligence, much more potential lies in the histopathology images of HE slices in primary gliomas could be excavated to aid prediction of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, histopathology image data and with sufficient sample size for deep learning (>1000) provide considerable opportunities for personalized prediction of molecular pathology with non-invasiveness and precision.
Tracking Information
- NCT #
- NCT04217044
- Collaborators
- Sun Yat-sen University
- Investigators
- Not Provided
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