A new imaging system to identify tumors in the pancreas and digestive system.
Official title: Geles: A Novel Imaging Informatics System for Generalizable Lesion Identification in Neuroendocrine Tumors
Funded at University of Colorado Denver · NIH-10740578 · Data updated 2026-11-12
This study is working on a new computer system that uses advanced imaging technology to help doctors find and measure gastroenteropancreatic neuroendocrine tumors more accurately, so patients can get better treatment options and care.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Colorado Denver NIH-funded |
| Lab location | 1 site (Aurora, UNITED STATES) |
| Project start | September 19, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $388,467 |
| NIH project number | 1R21CA274487-01A1 |
| Project ID | NIH-10740578 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with gastroenteropancreatic neuroendocrine tumors who require accurate assessment of their disease.
✕Less likely to help
Patients with neuroendocrine tumors that do not express somatostatin receptor subtype 2 may not benefit from this research.
What this research studies
This research focuses on improving the detection of gastroenteropancreatic neuroendocrine tumors (GEP-NETs), which are often diagnosed at advanced stages. The project aims to develop an automated imaging informatics system that utilizes deep learning to enhance the identification and quantification of these tumors using advanced PET/CT imaging techniques. By addressing the challenges of dataset shifts, the research seeks to create a more reliable tool for assessing tumor burden, which is crucial for developing effective treatment strategies. Patients may benefit from more accurate monitoring of their disease, leading to better-informed treatment decisions.
Why it matters
Potential benefit: If successful, this research could lead to improved detection and monitoring of neuroendocrine tumors, enhancing treatment outcomes for patients.
How similar studies have gone: Other research has shown promise in using deep learning for automated lesion detection, but this specific approach addressing dataset shifts is relatively novel.
Where this research is happening
-
University of Colorado Denver
Aurora, United States Active
Researchers
- Principal investigator: Xing, Fuyong — University of Colorado Denver
- Study coordinator: Xing, Fuyong
Related research
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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.