Many middle-aged and older women have mammograms every one to two years to screen for breast cancer, as recommended by their doctors. A study conducted by researchers at Washington University School of Medicine in St. Louis suggests that earlier mammograms contain underutilized data that can help identify women at high risk for breast cancer and even detect potentially affected breasts.
When doctors read mammograms, they evaluate breast density along with signs of cancer, and compare a woman’s previous mammograms to her most recent one to look for troubling changes. But some changes are difficult to detect by eye.
In the study, the researchers used a mathematical model to monitor changes in breast density over a decade in nearly 1,000 women and found that the rate of change differed significantly between the nearly 300 women who were later diagnosed with cancer and those who were not diagnosed with cancer. . Results are available online at Oncology GammaIt could help improve existing risk algorithms and aid efforts to identify women who could benefit from additional screening.
The best tool we have against breast cancer is early detection. By adding the change in intensity across repeated images to risk-rating models in each breast, we paved the way for better risk estimation with each updated mammogram. We can then better classify future risks and refer women to appropriate prevention strategies such as enhanced screening as part of routine breast health services.”
Graham A. Colditz, MD, MD, senior author, co-director of the Siteman Cancer Center at Barnes-Jewish Hospital and University of Washington School of Medicine
Doctors estimate a woman’s risk of developing breast cancer using factors including age, family history, high-risk genetic variants, and breast density. Women deemed to be at high risk are referred for complementary screening, which usually means an annual magnetic resonance imaging (MRI) scan in addition to an annual mammogram.
No one really knows why women with denser breasts tend to get bigger Likely to get breast cancer. First author Xu Jiang, Ph.D. -; Associate Professor of Surgery in the Department of Public Health Sciences in the Department of Surgery, and Siteman Research Member – ; He saw repeat mammography as an underutilized source of data on breast density and how it changes over time in an individual breast that may shed light on the relationship between density and disease.
She analyzed data for women in the Joan Knight Breast Health Group at the Siteman Cancer Center. The group was set up by Colditz, professor of surgery at Nice-Gene and director of the Department of Public Health Sciences, and colleagues in 2008 to study risk factors and improve breast cancer risk prediction models. It includes a diverse group of more than 10,000 women who did not have cancer when they joined.
Jiang identified 289 women in the group who developed cancer and compared them to 658 similar women in the group who did not develop cancer. Each woman received regular mammograms, so Jiang was able to collect and analyze a total of 8,710 images of a single breast, an average of four time points over a 10-year period for each woman.
Because breast cancer rarely appears in both breasts at the same time, Jiang analyzed the images of each breast separately. Women’s breasts usually become less dense as they age, but Jiang discovered that the density decreased significantly more slowly in breasts that later developed cancer than in breasts that did not.
“In the future, I believe we can use a woman’s previous density history, in addition to the current density estimate, to better understand the level of risk,” Jiang said. “We might even be able to tell which breast will be affected, because the density signal is strongest in the breast that develops for cancer. Many women already have mammograms, so data on density in each breast is already being collected. We just need to use the data sparingly.” more effective “.
Colditz, Jiang, and colleagues are now working to translate the findings into a form that can be used to enhance patient care. They are developing prediction models that incorporate change in breast density over time, and plan to validate the models on independent datasets so they can be used in clinical care.