Personalized CT follow-up for lung cancer survivors using imaging biomarkers and AI
Official title: OPTimizing surveillance in lung cancer survivors with novel IMAging biomarkers and deep-Learning (OPTIMAL)
Funded at Univ of North Carolina Chapel Hill · NIH-11235901 · Data updated 2026-11-12
This project uses routine chest CT scans and artificial intelligence to predict which early-stage lung cancer survivors are more likely to have the cancer come back or develop a new lung cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | January 1, 2025 |
| Project end | December 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $927,468 |
| NIH project number | 5R01CA298002-02 |
| Project ID | NIH-11235901 on NIH RePORTER |
Who could benefit from this research
✓Could help
People who have undergone curative-intent surgery for early-stage non-small cell lung cancer and who receive routine postoperative chest CT surveillance are the ideal candidates.
✕Less likely to help
Patients with advanced-stage lung cancer, those not having postoperative chest CT scans, or those without available prior CT images are unlikely to benefit directly from this project.
What this research studies
If you had surgery for early-stage non-small cell lung cancer, this project looks at your regular chest CT scans to find imaging signs (like muscle and fat measures and other features) that relate to risk. The team will apply deep-learning methods to CT images and combine that with clinical information to build risk models for recurrence or second primary lung cancer. Their plan is to use only scans already taken during follow-up, so no extra tests or risks for you. The goal is to tailor how often people get CT surveillance after surgery so higher-risk survivors are watched more closely and lower-risk survivors may avoid unnecessary scans.
Why it matters
Potential benefit: Could help detect recurrences earlier for higher-risk survivors and reduce unnecessary scans for lower-risk survivors by personalizing follow-up schedules.
How similar studies have gone: Prior studies, including work from these investigators, show CT-derived body composition and imaging biomarkers can predict outcomes after NSCLC surgery, but applying deep learning to personalize surveillance timing is a newer application.
Where this research is happening
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Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Henderson, Louise — Univ of North Carolina Chapel Hill
- Study coordinator: Henderson, Louise
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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.