MR 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
- Type
- Observational
- Design
- Observational Model: CohortTime Perspective: Retrospective
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 preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging,...
Non-invasive and precise prediction for survivals of glioma patients is challenging. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging) 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, radiological 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 #
- NCT04215211
- Collaborators
- Sun Yat-sen University
- Investigators
- Not Provided
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