AI-assisted radiotherapy planning for cervical and head and neck cancers
ARCHERY: Artificial Intelligence based Radiotherapy treatment planning for Cervical and Head and Neck cancer
This project uses artificial intelligence to create radiotherapy treatment plans faster and with less specialist time for people with cervical and head and neck cancers, especially in low- and middle-income countries.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University College London NIH-funded |
| Lab location | 1 site (London, United Kingdom) |
| Project ID | NIH-11399434 on NIH RePORTER |
What this research studies
The project uses AI software to automate two key radiotherapy planning steps: outlining the tumor and nearby organs, and defining beam positions and shapes. Patients treated at participating hospitals will receive AI-generated plans that are reviewed and approved by clinical teams. The team will run a forward-looking (non-randomised prospective) study enrolling 706 patients to compare plan quality, time to deliver a plan, and the cost of planning. The study focuses on settings where radiotherapy access is limited and aims to cut planning time from weeks to less than a day while keeping treatments accurate.
Who could benefit from this research
Good fit: People with cervical cancer or head and neck cancer who need radiotherapy and are treated at one of the participating clinical sites (including centers serving low- and middle-income countries) are ideal candidates.
Not a fit: Patients whose care is not at a participating center or whose treatment requires highly individualized experimental planning may not see direct benefit from this project.
Why it matters
Potential benefit: If successful, this could speed up planning, reduce the need for scarce specialist staff, and expand access to curative radiotherapy in places with limited resources.
How similar studies have performed: Previous pilot studies of AI auto-contouring and automated planning have shown promise for saving time and producing acceptable plans, but large prospective data—especially from LMIC settings—is limited.
Where this research is happening
London, United Kingdom
- University College London — London, United Kingdom (Active)
Researchers
- Principal investigator: Aggarwal, Ajay — University College London
- Study coordinator: Aggarwal, Ajay
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.