Observational Study for Glioma Imaging and Genetics
This study, called ReGIT, is looking at how brain tumors called gliomas change over time and if special imaging scans can predict these changes. You could join if you are 18 to 89 years old, have a suspected or diagnosed glioma (a type of brain tumor), and haven't had treatment for it yet, except for a previous biopsy. The study uses PET scans with FET F-18 and O-15 Radioisotope, CT scans, and MRI scans before a biopsy. Researchers will compare these images with genetic information from your biopsy to see if the scans can predict tumor mutations. The goal is to see how well these imaging methods can classify tumors and to track survival. The current status of this study is unclear, and it plans to enroll 20 participants.
- Study design
- This is an observational study, meaning researchers will gather information without giving new treatments. It plans to include 20 participants.
- What's involved
- You would have two study visits, including a pregnancy test (if applicable), two PET-CT scans, an MRI scan, and blood draws. During your standard biopsy, samples will be collected for research.
- Compensation
- Not stated in the trial record.
- Follow-up
- Your care will be followed, and information from your regular treatments and brain scans will be collected after your biopsy, potentially until 2032.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Registering Genomics and Imaging of Tumors (ReGIT)
At a glance
Conditions
Where it's being run
1 sites across 1 statesStudy leadership
- Jason Parker, PhD · PRINCIPAL_INVESTIGATOR · Indiana University
Who to contact
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What this trial measures
- Classification sensitivityThrough study completion, an average of 1 year.
Statistical evaluation of the area under the ROC curve. 3.1.1 SMM classification sensitivity to \>80% 3.1.2 Reduce computation time by a factor of 10, and maintain accuracy (\>.95)
- Multivariate analysis2032
Statistically determine if MRI, and 15O-H2O and FET-PET (if available) independently predict multiscale properties and together improve classification accuracy and sensitivity for SMM compared to 5 baseline MR sequences
- Survival2032
Statistically determine if SMM classifications using MRI, and 15O-H2O and FET-PET (if available) can predict progression-free survival (PFS) and overall survival (OS), and individual voxel responses to chemotherapy and radiation therapy.