Many middle-aged and older women get mammograms each one to 2 years to screen for breast cancer, as really helpful by their doctors. A study by researchers at Washington University School of Medicine in St. Louis indicates that previous mammograms hold underutilized data that would help discover women at high risk of breast cancer and even reveal which breast is prone to be affected.

When doctors read mammograms, they assess breast density together with signs of cancer, comparing a lady’s previous mammograms to her most up-to-date one to search for worrisome changes. But some changes are difficult to detect by eye.

Within the study, researchers used a mathematical model to watch changes in breast density over the course of a decade in almost 1,000 women and located that the speed of change differed significantly between the nearly 300 women who were later diagnosed with cancer and people who weren’t. The findings, available online in JAMA Oncology, could help refine current risk algorithms and aid efforts to discover women who may benefit from additional screening.

Our greatest tool against breast cancer is early detection. By adding the change in density over repeated images to models for risk classification in each breast, we set the stage for a greater risk estimation with each updated mammogram. We will then higher classify future risk and refer women to appropriate prevention strategies comparable to enhanced screening as a part of routine breast health services.”

Graham A. Colditz, MD, DrPH, senior creator, associate director of Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine

Doctors estimate a lady’s risk of breast cancer using aspects including age, family history, presence of high-risk genetic variants and breast density. Women considered to be at high risk are referred for supplemental screening, which usually means annual magnetic resonance imaging (MRI) scans along with annual mammograms.

No one really knows why women with denser breasts usually tend to develop breast cancer. First creator Shu Jiang, PhD -; an associate professor of surgery within the Division of Public Health Sciences throughout the Department of Surgery, and a research member at Siteman -; saw in repeated mammograms an untapped source of knowledge on breast density and the way it changes over time in individual breasts which may make clear the connection between density and disease.

She analyzed data on women within the Joanne Knight Breast Health Cohort at Siteman Cancer Center. The cohort was established by Colditz, the Niess-Gain Professor of Surgery and director of the Division of Public Health Sciences, and colleagues in 2008 to review risk aspects and improve models for breast cancer risk prediction. It comprises a various group of greater than 10,000 women who were free from cancer after they joined.

Jiang identified 289 women within the cohort who had developed cancer and compared them to 658 similar women within the cohort who didn’t. Each woman had received regular mammograms, so Jiang was in a position to collect and analyze a complete of 8,710 single-breast images, representing a median of 4 time points over a 10-year period for every woman.

Since breast cancer rarely develops in each breasts at the identical time, Jiang analyzed the pictures of every breast individually. Women’s breasts normally turn into less dense as they age, but Jiang discovered that density declined significantly more slowly within the breasts that later developed cancer than in people who didn’t.

“In the longer term, I feel we will use a lady’s past history of density, plus her current density estimate, to raised understand her risk level,” Jiang said. “We may even have the option to find out which breast will likely be affected, since the density signal is strongest within the breast that goes on to develop cancer. Many ladies already get regular mammograms, so the information on density in each breast is already being collected. We just need to make use of the information more effectively.”

Colditz, Jiang and colleagues are actually working on translating the findings right into a form that will be used to boost patient care. They’re developing prediction models that incorporate change in breast density over time, and plan to validate the models in independent data sets in order that they will be utilized in clinical care.

Source:

Washington University School of Medicine

Journal reference:

Jiang, S., et al. (2023). Longitudinal Evaluation of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk. JAMA Oncology. doi.org/10.1001/jamaoncol.2023.0434.