Using machine learning to improve treatment decisions for colorectal cancer patients after surgery
Official title: Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancer
Funded at Stanford University · NIH-11066505 · Data updated 2026-11-12
This study is looking to improve treatment decisions for patients with colorectal cancer who have had surgery by using smart computer technology to analyze their medical information and images, helping doctors find out who might need extra therapy to prevent the cancer from coming back.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | May 2, 2023 |
| Project end | April 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $624,102 |
| NIH project number | 5R01CA270437-03 |
| Project ID | NIH-11066505 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with Stage I-III colorectal cancer who have undergone surgical resection.
✕Less likely to help
Patients with metastatic colorectal cancer or those who have not undergone surgery may not benefit from this research.
What this research studies
This research aims to enhance the decision-making process for adjuvant therapy in patients with surgically resectable colorectal cancer by utilizing advanced machine learning techniques. The study will create a comprehensive dataset that includes pathology images, radiologic scans, and electronic medical records to develop predictive models. By integrating these diverse data sources, the research seeks to identify patients at higher risk of cancer recurrence who would benefit most from additional treatment. Patients will be evaluated based on their unique clinical profiles, allowing for more personalized treatment plans.
Why it matters
Potential benefit: If successful, this research could lead to more accurate treatment recommendations, potentially improving survival rates for colorectal cancer patients.
How similar studies have gone: Previous research has shown promise in using machine learning for cancer treatment planning, indicating that this approach could yield significant advancements.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Shen, Jeanne — Stanford University
- Study coordinator: Shen, Jeanne
Related research
Other NIH-funded projects on the same topics.
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AI that combines scans, pathology slides, and records to guide post-surgery treatment for colorectal cancer
This project uses artificial intelligence to combine CT/MRI scans, pathology slides, and medical records to help doctors decide which people with stage I–III colorectal cancer should get additional treatment after surgery.
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Improving colorectal cancer screening and follow-up care.
This study is working to help more people get screened for colorectal cancer and receive the follow-up care they need, especially those who haven't been screened much before, so that everyone can have better access to important health checks and improve their overall health.
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Colorectal cancer prognosis through a broad, collaborative study
A large, multi-center study follows people newly diagnosed with colorectal cancer to find biomarkers and patterns that predict outcomes and guide future care.
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Ceramides and how they affect metabolism and colorectal cancer in adults
A study in adults with obesity-related health issues will explore how a fat-related molecule called ceramide influences metabolic problems and colorectal cancer risk.
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Automated point-of-care DNA methylation test for colorectal cancer in LMICs
A fast, affordable blood test using DNA markers to help identify people at higher risk of colorectal cancer in low- and middle-income countries.
-
Rewiring Colorectal Cancer to Harness Therapeutic Hyperactivation
A Yale team is exploring brain-storming new ways to treat colorectal cancer by safely boosting certain cancer signaling pathways to kill cancer cells and by reshaping cancer cell states to improve responses to existing therapies.
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.