AI to find early signs of pancreatic cancer on routine CT scans
Predicting Pancreatic Ductal Adenocarcinoma (PDAC) Through Artificial Intelligence Analysis of Pre-Diagnostic CT Images
Uses artificial intelligence to look at routine abdominal CT scans and medical information to find people likely to develop pancreatic cancer within three years.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cedars-Sinai Medical Center NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project ID | NIH-11172502 on NIH RePORTER |
What this research studies
You can have past abdominal CT scans and health information analyzed by AI to look for early signs of pancreatic cancer that radiologists might miss. The team trains computer models on scans from people who later developed pancreatic cancer to learn subtle changes that appear years before a diagnosis. If the AI flags you as high risk, your doctors could offer earlier follow-up imaging or tests. Because many people get CT scans for abdominal pain in the ER, the project can use images people already have on file.
Who could benefit from this research
Good fit: Ideal candidates are people who have prior abdominal CT scans available (for example after ER visits for abdominal pain) and who are concerned about pancreatic cancer risk.
Not a fit: People without prior CT imaging available or those already diagnosed with pancreatic cancer are unlikely to benefit from this project.
Why it matters
Potential benefit: Could help detect pancreatic cancer earlier when surgery is possible, improving survival odds.
How similar studies have performed: AI tools have shown promise detecting subtle cancer signs in other organs, but using pre-diagnostic CT scans to predict pancreatic cancer is relatively new and still being tested.
Where this research is happening
Los Angeles, United States
- Cedars-Sinai Medical Center — Los Angeles, United States (Active)
Researchers
- Principal investigator: Li, Debiao — Cedars-Sinai Medical Center
- Study coordinator: Li, Debiao
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.