Using deep learning to identify different types of tumor cells from microscope images
Official title: IMAT-ITCR Collaboration: Develop deep learning-based methods to identify subtypes of circulating tumor cells from optical microscope images
Funded at Texas Tech University · NIH-10675886 · Data updated 2026-11-12
This study is working on a new computer program that helps doctors quickly and easily identify different types of cancer cells in blood samples, which could lead to better ways to diagnose and treat cancer for patients.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Texas Tech University NIH-funded |
| Lab location | 1 site (Lubbock, United States) |
| Project start | September 1, 2022 |
| Project end | July 31, 2025 |
| Funding year | 2022 |
| Award (this year) | $71,919 |
| NIH project number | 3R21CA240185-01A1S1 |
| Project ID | NIH-10675886 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients with circulating tumor cells present in their blood, particularly those with metastatic cancer.
✕Less likely to help
Patients without circulating tumor cells or those with non-metastatic cancer may not receive any benefit from this research.
What this research studies
This research focuses on developing advanced deep learning techniques to automatically identify and classify subtypes of circulating tumor cells (CTCs) captured in optical microscope images. By creating an informatics platform called iSEE-Cell, the project aims to streamline the analysis of these cells, which is currently a labor-intensive process requiring manual image recording and analysis. The goal is to enhance the understanding of the metastatic potential of CTCs, which could lead to better cancer diagnostics and treatment strategies. Patients may benefit from improved methods of identifying cancer cell types, which can inform personalized treatment options.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and efficient identification of cancer cell subtypes, ultimately improving patient outcomes through tailored therapies.
How similar studies have gone: Other research has shown promise in using deep learning for image analysis in medical applications, indicating potential success for this approach.
Where this research is happening
-
Texas Tech University
Lubbock, United States Active
Researchers
- Principal investigator: Li, Wei — Texas Tech University
- Study coordinator: Li, Wei
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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.