Using advanced imaging techniques to enhance kidney cancer treatment effectiveness
Official title: Employing quantitative image analysis based on deep learning to improve treatment efficacy in image-guided renal tumor ablation
Funded at Brown University · NIH-10954040 · Data updated 2026-11-12
This study is working on making a special cancer treatment called image-guided thermal ablation (IGTA) better for patients who can't have surgery or radiation, by using advanced computer technology to help doctors find and treat kidney tumors more accurately.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brown University NIH-funded |
| Lab location | 1 site (Providence, United States) |
| Project start | July 3, 2024 |
| Project end | June 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $164,639 |
| NIH project number | 1R03CA286693-01A1 |
| Project ID | NIH-10954040 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients with renal tumors who are not eligible for traditional surgical or radiotherapy options.
✕Less likely to help
Patients with non-renal tumors or those who are candidates for conventional surgical treatments may not benefit from this research.
What this research studies
This research focuses on improving the effectiveness of image-guided thermal ablation (IGTA), a minimally invasive cancer treatment for patients who may not be suitable for surgery or radiotherapy. By employing deep learning techniques for quantitative image analysis, the project aims to accurately identify treatment boundaries and reduce recurrence rates of renal tumors. The study will develop an automated system for kidney segmentation and lesion detection, enhancing the precision of the treatment process and follow-up care. This innovative approach seeks to make IGTA a more viable option for a broader range of patients.
Why it matters
Potential benefit: If successful, this research could lead to more effective kidney cancer treatments, resulting in improved survival rates and quality of life for patients.
How similar studies have gone: Previous research has shown promise in using deep learning for medical imaging, indicating that this approach could lead to significant advancements in cancer treatment.
Where this research is happening
-
Brown University
Providence, United States Active
Researchers
- Principal investigator: Kimia, Benjamin — Brown University
- Study coordinator: Kimia, Benjamin
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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.