AI tools to improve checks of radiation therapy plans
Official title: Development of AI-Augmented quality assurance tools for radiation therapy
Funded at Stanford University · NIH-11289312 · Data updated 2026-11-12
Using artificial intelligence to speed up and strengthen safety checks of radiation treatment plans for people getting external beam radiotherapy.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | February 1, 2023 |
| Project end | January 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $506,276 |
| NIH project number | 5R01CA275772-04 |
| Project ID | NIH-11289312 on NIH RePORTER |
Who could benefit from this research
✓Could help
People receiving external beam radiation therapy (especially IMRT/VMAT) at participating clinics, whose treatment plans and imaging can be reviewed, would be the ideal candidates.
✕Less likely to help
Patients not receiving external beam photon therapy (for example those only receiving brachytherapy or other non‑photon treatments) may not benefit from these specific tools.
What this research studies
This project will build AI (deep learning) tools that automatically check radiation treatment plans before you are treated. The tools will read the planned machine settings (like multileaf collimator shapes and monitor units) and predict the 3‑D dose on your treatment CT to look for mismatches or errors. The system will also cross‑check by translating predicted dose back into machine parameters to catch inconsistencies. The goal is a faster, more reliable QA step that could fit into routine clinic workflows.
Why it matters
Potential benefit: If successful, this could make radiation therapy safer and less prone to delays by catching planning or delivery problems earlier and reducing manual QA work.
How similar studies have gone: Some AI methods have shown promise for parts of radiation QA, but this project’s combined machine‑parameter and 3‑D dose cycle‑consistency approach is a novel, first‑of‑its‑kind effort.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Xing, Lei — Stanford University
- Study coordinator: Xing, Lei
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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.