AI tools to improve checks of radiation therapy plans
Development of AI-Augmented quality assurance tools for radiation therapy
['FUNDING_R01'] · STANFORD UNIVERSITY · NIH-11289312
Using artificial intelligence to speed up and strengthen safety checks of radiation treatment plans for people getting external beam radiotherapy.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | STANFORD UNIVERSITY (nih funded) |
| Locations | 1 site (STANFORD, UNITED STATES) |
| Trial ID | NIH-11289312 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: 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.
Not a fit: 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.
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 performed: 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, UNITED STATES
- STANFORD UNIVERSITY — STANFORD, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: XING, LEI — STANFORD UNIVERSITY
- Study coordinator: XING, LEI
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 below.