Deep learning for outlining head and neck tumors in radiation therapy
Official title: Deep learning-based target volume delineation capturing observer variability in head and neck cancer
Funded at Yale University · NIH-11318981 · Data updated 2026-11-12
A Yale-led project uses artificial intelligence to help doctors draw more accurate tumor boundaries in head and neck cancer so patients receive precise radiation doses.
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 | 2026 |
| Award (this year) | $537,025 |
| NIH project number | 5R01CA290745-03 |
| Project ID | NIH-11318981 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with head and neck cancer who are undergoing radiation therapy and whose imaging data can be used to train or test AI delineation models.
✕Less likely to help
Patients whose tumors or imaging data are not suitable for AI-based delineation or who are not receiving radiation therapy may not benefit.
What this research studies
This project develops robust deep learning tools to automatically outline target volumes in head and neck cancer using imaging data like CT and PET/CT. It aims to improve consistency across clinicians and reduce planning time while identifying factors that predict recurrence. The approach emphasizes handling the complex anatomy of the head and neck and extending beyond small regions to include common subsites such as laryngeal and nasopharyngeal cancers. Researchers will evaluate how these AI-generated delineations relate to treatment outcomes and recurrence patterns in real patients. The goal is to support safer, more effective radiation therapy planning for individuals with head and neck cancer.
Why it matters
Potential benefit: If successful, the approach could make radiation therapy planning faster and more accurate, potentially improving tumor control and reducing side effects for patients.
How similar studies have gone: Previous AI methods have improved GTV delineation in some cancers, but this project seeks to extend accuracy to additional subsites and CTV delineation with stronger validation.
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
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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.