By Kathleen Berger, Executive Producer of Science and Technology

A new clinical trial for AI-based breast cancer prediction technology will focus on women with dense breast tissue. Dense tissue makes it harder to find breast cancer on a mammogram and also raises the risk of developing breast cancer.

“In the trial that we’re designing, we’re looking at women with dense breast tissue,” said Debbie Bennett, MD, Breast Radiologist at Siteman Cancer Center and the Ronald and Hanna Evens Professor of Women’s Health and Section Chief of Breast Imaging at WashU Medicine in St. Louis. “Dense breast tissue is super common. It’s in almost half of all women that we screen.”

Bennett is leading an upcoming clinical trial to help advance groundbreaking breast cancer risk prediction technology developed at WashU Medicine by Graham A. Colditz, MD, DrPH, and Shu (Joy) Jiang, PhD. Colditz is the Niess-Gain Professor of Surgery at WashU Medicine and associate director of prevention and control at Siteman Cancer Center, based at Barnes-Jewish Hospital and WashU Medicine. Jiang is an associate professor of surgery in the Division of Public Health Sciences in the Department of Surgery at WashU Medicine.

Harnessing AI, the software is applied to mammograms. It’s designed to analyze mammograms and improve the accuracy of predicting a woman’s personalized five-year risk of developing breast cancer. In 2025, the technology received Breakthrough Device designation from the Food and Drug Administration (FDA). The designation provides an expedited review process for full market approval.

“We’ve done several validations on a global scale,” said Jiang. “Our tool can achieve up to 85% accuracy. It’s over twice as more accurate, compared to questionnaires alone.”

The new risk prediction technology may catch breast cancer very early for women at high risk of developing breast cancer. It may also reduce the need for additional imaging for all women at different times in their lives for different reasons, to include women at high risk or low risk for developing breast cancer.

“I’ll call it a giant leap forward towards personalized prevention strategies,” said Colditz. “With millions of bits of data from the mammogram, we get a much richer summary of the breast tissue than a four-point scale on density.”