Improving PET/CT imaging for better lymphoma treatment decisions
Official title: Multimodal Learning for Contextually-Aware Longitudinal PET/CT image analysis
Funded at University of Wisconsin-Madison · NIH-10988306 · Data updated 2026-11-12
This study is working on using smart computer programs to better analyze PET/CT scans for lymphoma patients, helping doctors get more accurate information about how well treatments are working, so patients can receive more tailored and effective care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Wisconsin-Madison NIH-funded |
| Lab location | 1 site (Madison, United States) |
| Project start | December 1, 2023 |
| Project end | November 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $535,178 |
| NIH project number | 5R01EB033782-02 |
| Project ID | NIH-10988306 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults diagnosed with lymphoma who are undergoing treatment and require PET/CT imaging for evaluation.
✕Less likely to help
Patients with lymphoma who are not receiving PET/CT imaging or those with other unrelated conditions may not benefit from this research.
What this research studies
This research focuses on enhancing the analysis of PET/CT imaging for lymphoma patients by developing advanced deep learning algorithms. These algorithms will analyze images taken at multiple time points to provide more accurate and context-aware evaluations of treatment responses. By moving beyond traditional visual scoring methods, which can be subjective and inconsistent, this project aims to automate and improve the accuracy of quantitative assessments of lymphoma. Patients may benefit from more personalized and effective treatment plans based on these improved imaging analyses.
Why it matters
Potential benefit: If successful, this research could lead to more accurate diagnoses and treatment adaptations for lymphoma patients, ultimately improving their outcomes.
How similar studies have gone: Other research has shown promise in using deep learning for medical imaging analysis, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
-
University of Wisconsin-Madison
Madison, United States Active
Researchers
- Principal investigator: Bradshaw, Tyler J — University of Wisconsin-Madison
- Study coordinator: Bradshaw, Tyler 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.