Improving lung cancer radiation treatment with AI
Official title: Safer lung cancer radiotherapy delivery using novel artificial intelligence methods
Funded at Sloan-Kettering Inst Can Research · NIH-11089500 · Data updated 2026-11-12
This work aims to make radiation treatment for lung cancer safer and more effective by using new artificial intelligence tools.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Sloan-Kettering Inst Can Research NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | June 15, 2022 |
| Project end | May 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $560,469 |
| NIH project number | 5R01CA258821-04 |
| Project ID | NIH-11089500 on NIH RePORTER |
Who could benefit from this research
✓Could help
This research is relevant for lung cancer patients who are receiving or will receive radiation therapy as part of their treatment plan.
✕Less likely to help
Patients with lung cancer who are not undergoing radiation therapy would not directly benefit from this specific improvement in radiation delivery.
What this research studies
Lung cancer is a serious disease, and radiation combined with chemotherapy is a common treatment for those who cannot have surgery. Unfortunately, this treatment can sometimes lead to the cancer coming back and may cause side effects to healthy organs due to radiation spillover. This project is developing a new artificial intelligence (AI) method called CMEDL to precisely locate tumors and nearby healthy organs during radiation treatment. By using AI to guide the radiation, we hope to deliver more accurate treatment and reduce harm to healthy tissues.
Why it matters
Potential benefit: If successful, this work could lead to more precise and safer radiation treatments for lung cancer patients, potentially reducing side effects and improving outcomes.
How similar studies have gone: This project proposes a novel AI methodology, Cross-Modality Educed Learning (CMEDL), which builds upon existing AI approaches for medical imaging but introduces a new way to combine different types of scans for better accuracy.
Where this research is happening
-
Sloan-Kettering Inst Can Research
New York, United States Active
Researchers
- Principal investigator: Veeraraghavan, Harini — Sloan-Kettering Inst Can Research
- Study coordinator: Veeraraghavan, Harini
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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.