For generations, breast cancer screening has followed a familiar path. Most women begin routine mammograms around age 40, following recommendations based on broad population data.
But what if screening could be tailored to you? What if doctors could better predict who is most likely to develop breast cancer—not only by looking at family history, but by combining genetic and imaging data with the power of artificial intelligence?
That is the question Lynn Sage Scholar Dr. Dezheng Huo is working to answer. His research is exploring a future where breast cancer screening is personalized for every woman based on her unique biology and risk.
A Smarter Way to Assess Risk
Breast cancer does not affect every woman in the same way. Some women develop cancer at a younger age. Others face more aggressive forms of the disease. Many have no family history at all. Factors like breast density can also make cancers more difficult to detect on a mammogram, underscoring the need for screening strategies that consider more than age alone. Yet many screening recommendations still rely primarily on age, rather than on a woman’s individual risk factors.
Dr. Huo believes we can do better. His Lynn Sage-funded research— Using Artificial Intelligence to Integrate Imaging and Genetic Markers for Personalized Breast Cancer Risk Prediction—is bringing together two powerful technologies to create a more complete picture of breast cancer risk.
The first is genetics. Our DNA contains thousands of tiny genetic variations that, when considered together, can help estimate a person’s likelihood of developing breast cancer. While many are familiar with single genes that carry genetic risk, such as BRCA, newer models, such as Dr. Huo’s, look at polygenic risk scores, which estimate a woman’s inherited risk based on multiple genetic variants across a woman’s genome.
The second is artificial intelligence analysis of routine medical imaging, such as mammograms. Using advanced AI, computers can analyze mammograms and identify subtle patterns that may be invisible to the human eye—clues that could provide valuable insight into future breast cancer risk.
On their own, each tool tells part of the story. Together, they have the potential to transform how breast cancer risk is understood.
Dr. Huo is developing more accurate risk predictions models by combining polygenic risk scores with AI-derived deep-learning scores from mammograms to improve our understanding of breast cancer risk and unlock insights that can personalize care. Drawing on genetic data from more than 4,300 women and some 82,000 mammogram images his team has already published a new set of polygenic risk scores built specifically for women of African ancestry, a population long underserved by existing tools, and developed a powerful mammography AI pipeline. The project is now in its validation stage, with the team testing each component and beginning to integrate them, measuring how the combined model performs across diverse racial and ethnic groups and tumor subtypes. His aim is a single, more precise, and more equitable measure of individual risk — one that lets screening and prevention be tailored to each woman rather than the average.
When Technology Meets Prevention
Imagine a future where your doctor can combine your genetic profile with information from your mammogram to better understand your individual risk. That knowledge could help answer important questions:
- Should you begin screening earlier?
- Would more frequent imaging provide greater protection?
- Could additional preventive strategies reduce your risk before cancer develops?
Rather than relying on a single recommendation for everyone, physicians could make more personalized decisions based on each woman’s individual risk profile.
The goal isn’t simply finding cancer earlier. It’s identifying risks earlier, before cancer has the opportunity to grow.
Research That Looks Ahead
Artificial intelligence is already changing many areas of medicine, but its greatest impact may come from helping physicians make more informed, individualized decisions.
That reflects something fundamental about scientific progress.
Breakthroughs don’t happen because researchers accept the way things have always been done. They happen because someone asks a new question – and Dr. Huo’s question is whether technology can help physicians see risk sooner and act on it.
Every new discovery builds upon the work that came before it, opening doors to questions that once seemed impossible to answer.
Dr. Huo’s work represents that next chapter – research with the potential to reshape how breast cancer risk is assessed and how screening is delivered in the years ahead.
At Lynn Sage, investing in innovative research means supporting bold ideas before they become tomorrow’s standard of care. Because the future of breast cancer screening won’t be one-size-fits-all. It will be personal.