Persistent or increasing breast density linked to higher cancer risk

By Dr. Chinta SidharthanReviewed by Danielle Ellis, B.Sc.Jan 6 2025

Using mammographic data from over 1.7 million women aged 40 and above, the researchers identified distinct patterns of density changes. The findings highlighted the importance of tracking density trajectories to enhance breast cancer risk prediction.

In a recent study published in BMJ, researchers investigated the relationship between changes in breast density over time and breast cancer risk.

Background

Breast density is a critical factor in breast cancer risk, as denser breasts increase the likelihood of cancer and complicate the efficient detection of tumors. Typically, breast density decreases with age, but certain patterns of increase are associated with heightened cancer risk.

Current screening programs often rely on single-time-point measurements of breast density, potentially overlooking significant changes over time. Such static measures may fail to capture the dynamic nature of breast density, especially in populations with routine mammographic surveillance.

Research has demonstrated the predictive value of longitudinal density patterns, but large-scale studies on density changes across multiple screenings are limited. Understanding these patterns could provide valuable insights for improving risk prediction and early detection strategies.

Exploring how breast density trajectories relate to cancer outcomes is also essential for refining screening protocols and tailoring interventions.

About the study

In the present study, the researchers analyzed data from over 1,700,000 women aged 40 and above who had undergone four consecutive biennial mammograms between 2009 and 2016 through South Korea’s national breast cancer screening program.

Breast density was evaluated using the Breast Imaging-Reporting and Data System or BI-RADS categories, namely, fatty, scattered fibroglandular, heterogeneously dense, and extremely dense. Participants with prior cancer or missing data were excluded.

The researchers applied group-based trajectory modeling to identify five distinct density-change patterns. They also collected data on covariates such as age, body mass index (BMI), menopausal status, reproductive history, and lifestyle factors through questionnaires and medical records.

Cancer diagnoses, which were confirmed using medical records and insurance data, were tracked until 2021. Additionally, diagnostic and rare disease codes were included in the analysis to enhance the accuracy of breast cancer identification.

By employing longitudinal data and robust statistical methods, the researchers aimed to develop a reliable method of identifying breast density patterns and their links to breast cancer risk.

Major findings

The study identified five distinct breast density trajectories and revealed their associations with breast cancer risk. The researchers observed that women with persistently low density (fatty breasts) had the lowest risk of breast cancer and used this group as the reference group in the study. In contrast, women with low breast density in the beginning but increasing breast density over time showed a 1.6-fold higher risk of breast cancer.

Among the trajectories that involved women with denser breast tissue, the groups with initially high density, either heterogeneous or extremely dense breast tissue, that decreased with time exhibited stable or slightly decreasing risk patterns over time.

However, all three groups had significantly elevated cancer risks compared to the reference group with low breast density. Women with persistently high breast density had a 3.07-fold greater risk of breast cancer compared to the reference group. These associations also persisted across age groups and were unaffected by changes in BMI or menopausal status.

Furthermore, subgroup analyses also revealed consistent patterns irrespective of BMI shifts or menopausal transitions. Women with persistently dense or increasing breast density faced higher risks in all scenarios. Additionally, sensitivity analyses, which included women with fewer screenings, confirmed the robustness of these findings and showed similar trajectories and risk patterns.

The results emphasized the significance of monitoring breast density changes over time. The researchers demonstrated that static density assessments may fail to capture the heightened risks associated with dynamic density patterns, particularly in women with increasing or persistently high breast tissue density. These insights also highlighted the potential benefits of incorporating longitudinal density tracking into breast cancer screening and risk prediction models.

Conclusions

Overall, the study established the importance of monitoring breast density changes over time in predicting breast cancer risk. Persistently high or increasing breast tissue density was linked to significantly higher cancer risks, which also indicated the limitations of static density measures.

The results showed that by integrating longitudinal density assessments into screening protocols, healthcare systems can improve risk prediction and early detection strategies. Furthermore, tailored interventions for high-risk women based on dynamic density changes could greatly enhance prevention efforts and clinical outcomes.