Predicting breast cancer recurrence and chemotherapy need using pathology images and AI
Official title: Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit
Funded at University of Chicago · NIH-11357620 · Data updated 2026-11-12
A doctor-scientist at a major university is developing an AI-based tool that uses pathology slides to predict which breast cancer patients will benefit from chemotherapy.
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 start | July 7, 2023 |
| Project end | June 30, 2028 |
| Funding year | 2026 |
| Award (this year) | $202,595 |
| NIH project number | 5K08CA283261-04 |
| Project ID | NIH-11357620 on NIH RePORTER |
Who could benefit from this research
✓Could help
Women with hormone receptor-positive breast cancer considering chemotherapy in addition to anti-estrogen therapy.
✕Less likely to help
Patients whose cancer biology does not align with the AI biomarker or who are not candidates for chemotherapy.
What this research studies
This project uses deep learning on pathology biopsy images to predict chemotherapy benefit and recurrence risk in hormone receptor-positive breast cancer. It aims to make a faster, cheaper biomarker than current tests by extracting information from standard biopsy slides. The work combines clinical data, tissue pathology, and immune features to improve decision-making about adding chemotherapy to hormonal therapy. If successful, the tool could reduce delays and expand access to precision treatment in diverse clinical settings.
Why it matters
Potential benefit: Could help doctors tailor chemotherapy decisions, potentially improving outcomes and reducing unnecessary treatment.
How similar studies have gone: Preliminary work shows promise for image-based biomarkers, but robust validation in diverse populations is still needed.
Where this research is happening
-
University of Chicago
Chicago, United States Active
Researchers
- Principal investigator: Howard, Frederick Matthew — University of Chicago
- Study coordinator: Howard, Frederick Matthew
Related research
Other NIH-funded projects on the same topics.
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Better Predicting Breast Cancer Recurrence and Chemotherapy Needs
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.
-
Using advanced technology to predict breast cancer recurrence and chemotherapy benefits
This study is looking to help doctors better predict if breast cancer might come back and how well chemotherapy will work by using advanced computer techniques to analyze tissue samples, making it easier and more affordable for patients to understand their treatment options.
-
A 3D ultrasound scanner to see early chemotherapy response in breast cancer
A new 3D ultrasound imaging method will be tested to see if it can quickly show whether breast cancer is responding to chemotherapy.
-
Gene expression marks high risk for very late breast cancer return
A patient-focused study aims to find gene patterns that identify women with early breast cancer who are most likely to have cancer come back more than a decade later.
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Using gene expression to identify patients at high risk for late breast cancer recurrence
Researchers are studying breast cancer patients to find gene patterns that predict who might have cancer come back many years later, so doctors can tailor care for those at highest risk.
-
Gene tests to find women at high risk of breast cancer returning many years later
This project looks for patterns in tumor gene activity to spot women with ER-positive breast cancer who are more likely to have the cancer come back more than 10 years after initial treatment.
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.