Using machine learning to analyze MRI scans in glioblastoma patients
Official title: Machine Learning and Radiomics Techniques for Analysis of Daily MRI in Glioblastoma Patients
Funded at University of Miami School of Medicine · NIH-10984449 · Data updated 2026-11-12
This study is looking at ways to better understand MRI scans for people with glioblastoma, a tough type of brain cancer, by using advanced technology to tell the difference between real tumor growth and changes that look like growth but aren’t, helping doctors make better treatment choices for patients.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Miami School of Medicine NIH-funded |
| Lab location | 1 site (Coral Gables, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $53,974 |
| NIH project number | 5F30CA278357-02 |
| Project ID | NIH-10984449 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are glioblastoma patients undergoing treatment who are experiencing changes in their MRI results.
✕Less likely to help
Patients with glioblastoma who are not receiving treatment or those with other types of brain tumors may not benefit from this research.
What this research studies
This research focuses on glioblastoma, a highly aggressive brain cancer, and aims to improve the interpretation of MRI scans post-treatment. By employing machine learning and radiomics techniques, the study seeks to differentiate between true tumor progression and pseudoprogression, which can mimic tumor growth on imaging. This differentiation is crucial as it can significantly impact treatment decisions and patient outcomes. The research will analyze daily MRI data to develop more accurate predictive models for patient management.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses and better treatment strategies for glioblastoma patients, potentially improving survival rates.
How similar studies have gone: Previous research has shown promise in using machine learning for medical imaging analysis, indicating potential success for this novel approach in glioblastoma.
Where this research is happening
-
University of Miami School of Medicine
Coral Gables, United States Active
Researchers
- Principal investigator: Cullison, Kaylie — University of Miami School of Medicine
- Study coordinator: Cullison, Kaylie
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.