Personalized AI predicts heart risks for older women with HER2-positive breast cancer
Official title: Personalized Prediction of HER2-Induced Cardiovascular Adverse Events in Patients with Breast Cancer Using Robust AI and Machine Learning Models
Funded at University of Texas at Austin · NIH-11445516 · Data updated 2026-11-12
A patient-facing AI-based approach aims to forecast who among older women with HER2-positive breast cancer will have heart problems from HER2-targeted therapy, so doctors can tailor treatment.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas at Austin NIH-funded |
| Lab location | 1 site (Austin, United States) |
| Project start | September 1, 2026 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $238,858 |
| NIH project number | 1R21HL184224-01A1 |
| Project ID | NIH-11445516 on NIH RePORTER |
Who could benefit from this research
✓Could help
Older women (65+) with HER2-positive breast cancer receiving or considered for HER2-targeted therapy.
✕Less likely to help
Patients who are not receiving HER2-targeted therapy or who do not have cardiovascular risk factors may see limited direct benefit.
What this research studies
This project plans to build robust AI and machine learning models using a wide range of patient data to predict cardiovascular adverse events in older women receiving HER2-targeted therapy. The researchers want models that perform well across diverse groups by including detailed clinical and demographic factors and even area-level healthcare information. The goal is to help clinicians choose safer, personalized treatment plans and prevent serious heart-related complications. The work will rely on existing patient data to train and test the predictive tools, with the ultimate aim of improving survival and quality of life for older breast cancer patients.
Why it matters
Potential benefit: If successful, this work could help doctors personalize therapy to minimize heart risks for older patients.
How similar studies have gone: Previous studies show mixed success; integrating more factors and area-level data is a novel approach that aims to improve prediction.
Where this research is happening
-
University of Texas at Austin
Austin, United States Active
Researchers
- Principal investigator: Park, Chanhyun — University of Texas at Austin
- Study coordinator: Park, Chanhyun
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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.