Using AI to Improve Colorectal Cancer Prognosis
Official title: An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.
Funded at Ohio State University · NIH-11128528 · Data updated 2026-11-12
This project is creating a smart computer system to find tiny groups of cancer cells in colon tissue, helping doctors better understand a patient's colorectal cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ohio State University NIH-funded |
| Lab location | 1 site (Columbus, UNITED STATES) |
| Project start | July 1, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $492,621 |
| NIH project number | 5R01CA276301-03 |
| Project ID | NIH-11128528 on NIH RePORTER |
Who could benefit from this research
✓Could help
This research could ultimately benefit patients diagnosed with colorectal cancer by offering a more precise way to understand their disease.
✕Less likely to help
Patients without colorectal cancer or those whose cancer has already been fully characterized by current methods may not directly benefit from this specific research.
What this research studies
Colorectal cancer is a serious illness, and doctors are always looking for better ways to predict how it might progress. One important clue is called "tumor budding," which refers to small clusters of cancer cells that can indicate a more aggressive form of the disease. Currently, finding these tiny cell groups can be challenging and inconsistent for pathologists. This project aims to develop an advanced computer system that uses artificial intelligence to accurately and consistently identify tumor buds in tissue samples. By standardizing this process, the system will help doctors make more informed decisions about treatment and patient care.
Why it matters
Potential benefit: If successful, this technology could provide more accurate and consistent information about colorectal cancer, leading to better treatment plans and improved patient outcomes.
How similar studies have gone: While the concept of using AI for pathology is growing, this specific approach to standardize tumor bud detection and correlate it with multiple patient outcomes is a novel and important step.
Where this research is happening
-
Ohio State University
Columbus, United States Active
Researchers
- Principal investigator: Chen, Wei — Ohio State University
- Study coordinator: Chen, Wei
Related research
Other NIH-funded projects on the same topics.
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Ensemble deep learning to detect tumor buds and judge risk in colorectal cancer
A computer tool will help doctors identify tumor buds in colorectal cancer slides and use that information to better estimate a patient’s prognosis.
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Using deep learning to detect tumor buds in colorectal cancer.
This study is working on a smart computer program that helps doctors spot tiny signs of tumor growth in colorectal cancer, which can help predict how well patients might do, so they can make better treatment choices.
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BVES protein in colorectal cancer: stopping tumor spread by stabilizing cell junctions
This grant explores how BVES, a protein at cell junctions, prevents colorectal cancer cells from becoming more aggressive and how restoring BVES could lead to new ways to stop tumor growth.
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A noninvasive brush-based test using artificial intelligence analyzes tiny RNA patterns to distinguish oral cancer from benign oral lesions, helping people avoid delays.
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extracellular RNA in colorectal cancer: how RNAs travel between cells and impact tumors
A broad research program at Vanderbilt is studying how extracellular RNAs travel between cancer cells in colorectal cancer and influence tumor growth and treatment response.
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Understanding how RNA communication affects colorectal cancer
This study is looking at how tiny pieces of RNA found outside of cells might affect the growth and spread of colorectal cancer, with the hope that understanding this could lead to new treatments for patients.
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.