Using advanced technology to predict breast cancer recurrence and chemotherapy benefits
Official title: Integrating Clinical, Pathologic, and Immune Features to Predict Breast Cancer Recurrence and Chemotherapy Benefit
Funded at University of Chicago · NIH-10881949 · Data updated 2026-11-12
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.
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 | 2024 |
| Award (this year) | $201,055 |
| NIH project number | 5K08CA283261-02 |
| Project ID | NIH-10881949 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are women diagnosed with hormone receptor-positive breast cancer who are at risk for recurrence and may benefit from chemotherapy.
✕Less likely to help
Patients with non-hormone receptor-positive breast cancer or those who are not at risk for recurrence may not benefit from this research.
What this research studies
This research aims to improve predictions of breast cancer recurrence and the effectiveness of chemotherapy by integrating clinical, pathologic, and immune features. It utilizes deep learning techniques to analyze pathology samples, which are routinely collected during diagnosis, to identify patterns that may indicate a patient's risk of recurrence. The goal is to create a more accessible and cost-effective method for assessing treatment options, particularly for patients who may not have access to expensive gene expression assays. By refining these predictive models, the research seeks to enhance decision-making for breast cancer treatment.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of breast cancer recurrence and better-targeted chemotherapy treatments, ultimately improving patient outcomes.
How similar studies have gone: Preliminary work has shown promise in using deep learning for similar predictive purposes, indicating potential for success in this novel approach.
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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Predicting breast cancer recurrence and chemotherapy need using pathology images and AI
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.
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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.
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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.
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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.
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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.