AI-Enhanced Perfusion MRI to Improve Y90 Particle Distribution in Liver Cancer Treatment
Official title: AI-Enhanced Perfusion MRI for Optimizing Y90 Particle Density in Radioembolization of Liver Cancer
Funded at University of California Los Angeles · NIH-11368811 · Data updated 2026-11-12
A cutting-edge imaging tool uses artificial intelligence to better predict how Y-90 beads spread in liver tumors to improve radioembolization therapy.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | May 15, 2026 |
| Project end | April 30, 2031 |
| Funding year | 2026 |
| Award (this year) | $640,956 |
| NIH project number | 1R01CA303795-01A1 |
| Project ID | NIH-11368811 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with primary or secondary liver cancer undergoing radioembolization with Y-90 and who have routine pre-treatment MRI data.
✕Less likely to help
Patients whose tumors or imaging data are not suitable for AI-based perfusion analysis may not benefit.
What this research studies
This project aims to create an AI-powered perfusion analysis method that uses routine MRI scans to map liver tumor blood flow and vascularity. By combining dynamic contrast-enhanced MRI data with deep learning, the tool will predict the distribution of Y-90 microspheres more accurately. The goal is to reduce under- and over-treatment by accounting for tumor flow dynamics during planning and treatment. The research will be conducted in a translational framework, linking imaging biomarkers to improved treatment delivery in liver cancer.
Why it matters
Potential benefit: If successful, patients could receive more precise Y-90 treatment with fewer cold spots or excessive embolization.
How similar studies have gone: Previous work shows AI and perfusion imaging can improve prediction of treatment distribution in liver tumors, but this project advances in vivo imaging biomarkers and AI optimization.
Where this research is happening
-
University of California Los Angeles
Los Angeles, United States Active
Researchers
- Principal investigator: Chiang, Chih-Sheng J — University of California Los Angeles
- Study coordinator: Chiang, Chih-Sheng J
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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.