AI-powered virtual 'digital twins' to make MRI-guided adaptive radiation safer for abdominal cancers
Artificial Intelligence powered virtual digital twins to construct and validate AI automated tools for safer MR-guided adaptive RT of abdominal cancers
This project builds AI-created virtual copies of patients to help doctors deliver safer, more precise MRI-guided adaptive radiation for people with abdominal cancers like pancreatic cancer.
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 ID | NIH-11159543 on NIH RePORTER |
What this research studies
If you participate, the team will create virtual digital twins — computer models that mimic how your tumor and nearby organs move during treatment — using MRI scans. They will develop AI tools that track how radiation dose adds up in moving organs across multiple treatment sessions. The work focuses on improving deformable image registration (matching images across time) so dose to sensitive gut organs can be estimated accurately. The tools will be tested and validated using the virtual twins and existing imaging data to make sure they are reliable before being used in care.
Who could benefit from this research
Good fit: Ideal candidates are people with abdominal cancers (for example, inoperable pancreatic cancer) who are receiving or may receive MRI-guided adaptive radiotherapy and can share their MRI images for analysis.
Not a fit: People with cancers outside the abdomen or those not treated with MRI-guided adaptive radiotherapy are unlikely to benefit directly from this work.
Why it matters
Potential benefit: If successful, this could let doctors target tumors more completely while lowering the risk of radiation damage to nearby gastrointestinal organs.
How similar studies have performed: MRI-guided adaptive radiotherapy and deformable image registration methods have shown promise, but using AI-built digital twins to automate and validate dose-accumulation tracking is a new and emerging approach.
Where this research is happening
New York, United States
- Sloan-Kettering Inst Can Research — New York, United States (Active)
Researchers
- Principal investigator: Tyagi, Neelam — Sloan-Kettering Inst Can Research
- Study coordinator: Tyagi, Neelam
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.