Better Predicting Breast Cancer Recurrence and Chemotherapy Needs
Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit
This project uses artificial intelligence to help predict which breast cancer patients might benefit most from chemotherapy and who is at risk for their cancer returning.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Chicago NIH-funded |
| Lab location | 1 site (Chicago, United States) |
| Project ID | NIH-11115736 on NIH RePORTER |
What this research studies
Breast cancer is a common disease, and while many patients respond well to anti-estrogen therapy, some face aggressive forms that require chemotherapy to prevent recurrence. Current tests to guide chemotherapy decisions can be expensive and slow, leading to delays in treatment or limited access. This project aims to develop a new, faster, and more affordable method using artificial intelligence to analyze routine biopsy images. By looking at these images, the AI can help doctors understand a patient's risk of recurrence and whether chemotherapy would be truly beneficial. This could lead to more personalized and timely treatment plans for breast cancer patients.
Who could benefit from this research
Good fit: Patients with hormone receptor-positive breast cancer who are considering chemotherapy would be the focus of this research.
Not a fit: Patients with breast cancer types other than hormone receptor-positive may not directly benefit from this specific prediction tool.
Why it matters
Potential benefit: If successful, this could provide a faster, more affordable, and more accurate way to decide if chemotherapy is needed for breast cancer patients, potentially reducing treatment delays and costs.
How similar studies have performed: While existing gene expression tests predict recurrence, this approach uses deep learning on pathology images to offer a novel, potentially more accessible, and refined prediction method.
Where this research is happening
Chicago, United States
- University of Chicago — Chicago, United States (Active)
Researchers
- Principal investigator: Howard, Frederick Matthew — University of Chicago
- Study coordinator: Howard, Frederick Matthew
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.