Using deep learning to improve radiation therapy for head and neck cancer
Official title: Deep learning-based target volume delineation capturing observer variability in head and neck cancer
Funded at Yale University · NIH-11077361 · Data updated 2026-11-12
This study is working on using advanced computer technology to help doctors better identify and outline tumors in patients with head and neck cancer, making radiation therapy more effective and improving treatment results.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | May 1, 2024 |
| Project end | April 30, 2029 |
| Funding year | 2025 |
| Award (this year) | $508,141 |
| NIH project number | 5R01CA290745-02 |
| Project ID | NIH-11077361 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with head and neck cancer who are undergoing radiation therapy.
✕Less likely to help
Patients with head and neck cancer who are not receiving radiation therapy or those with advanced metastatic disease may not benefit from this research.
What this research studies
This research focuses on developing advanced deep learning techniques to accurately outline tumor volumes in patients with head and neck cancer, which is crucial for effective radiation therapy. The project aims to enhance the precision of target volume delineation, addressing the challenges posed by the complex anatomy and high recurrence rates associated with this type of cancer. By leveraging 18F-FDG-PET/CT imaging, the research seeks to improve treatment planning and outcomes for patients undergoing radiation therapy. The methodology involves training algorithms to better predict tumor recurrence and optimize treatment delivery.
Why it matters
Potential benefit: If successful, this research could lead to more accurate radiation therapy planning, reducing the risk of cancer recurrence and improving patient outcomes.
How similar studies have gone: Previous research has shown promise in using deep learning for tumor delineation in other cancer types, indicating potential success for this novel approach in head and neck cancer.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: El Fakhri, Georges — Yale University
- Study coordinator: El Fakhri, Georges
Related research
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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.