Using AI to Guide Personalized Cancer Treatment
Interpretable deep learning models for translational medicine Renewal
This project is creating a smart computer system to help doctors choose the best chemotherapy for individual cancer patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project ID | NIH-11139557 on NIH RePORTER |
What this research studies
Current cancer treatments often rely on general guidelines, but this project aims to make them more personal. We are building an advanced artificial intelligence (AI) system that can learn from a patient's unique cancer information, like their genetic makeup. This AI will then predict how sensitive their cancer cells might be to different chemotherapy drugs. Our goal is to use this personalized information to help doctors decide the most effective treatment for colon cancer patients, especially those receiving chemotherapy after surgery.
Who could benefit from this research
Good fit: This research is particularly relevant for patients with colon cancer who are considering or undergoing adjuvant chemotherapy.
Not a fit: Patients with cancers other than colon cancer or those not receiving chemotherapy may not directly benefit from this specific application.
Why it matters
Potential benefit: If successful, this work could lead to more effective and personalized chemotherapy treatments, potentially improving outcomes for cancer patients.
How similar studies have performed: While precision oncology is an active field, this project proposes an advanced AI framework to personalize chemotherapy, which is a novel approach for this specific challenge.
Where this research is happening
Pittsburgh, United States
- University of Pittsburgh at Pittsburgh — Pittsburgh, United States (Active)
Researchers
- Principal investigator: Lu, Xinghua — University of Pittsburgh at Pittsburgh
- Study coordinator: Lu, Xinghua
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 below.