Histopathology Images Based Survival Prediction of Glioma Patients 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 90 years
- Gender
- Both males and females
Description
Non-invasive and precise prediction for survivals of glioma patients 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 patients' prognosis in the frame of mole...
Non-invasive and precise prediction for survivals of glioma patients 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 patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, histopathology image data and with sufficient sample size for deep learning (>1000) provides opportunities for personalized prediction of survival of glioma patients with non-invasiveness and precision.
Tracking Information
- NCT #
- NCT04215224
- Collaborators
- Sun Yat-sen University
- Investigators
- Not Provided
Get Well Soon