News | Breast Imaging | December 12, 2016

Most Women Unaware of Breast Density's Effect on Cancer Risk

breast cancer

Image courtesy of the UVA Center for Survey Research

Most women don't know that having dense breasts increases their risk for breast cancer and reduces a mammogram's ability to detect cancer, according to a University of Virginia School of Medicine study.

A random phone survey of 1,024 Virginia women ages 35 to 70, conducted by the UVA Center for Survey Research, found that just 1 in 8 women were aware that breast density is a risk factor for breast cancer, while just 1 in 5 women knew that dense breasts reduced the sensitivity of mammograms to find tumors.

"It is important for women to know whether or not their own breast density is classified into one of the two high-density categories since this will increase their breast cancer risk," said study co-author Wendy Cohn, Ph.D., an associate professor in UVA's Department of Public Health Sciences. "Women need to know whether their breast density will make it harder to detect breast cancer so that, along with their healthcare team, they can consider other options for screening and detection."

Virginia is among at least 27 states that require radiologists to tell women about their breast density, according to the study, and providing that information improves women's understanding of how breast density may impact their health.

The survey found that the strongest factor in knowing about breast density and its relationship with breast cancer was whether a healthcare provider had informed a woman about the density of her breasts. UVA researchers stressed the importance of a conversation between patients and healthcare providers about the impact of breast density.

"The most important thing that doctors and patients can take away from this study is that the required written notice about breast density isn't enough in itself: patients need to talk with their providers about what breast density means for each woman's individual breast cancer risk," said Thomas Guterbock, a professor of sociology and director of the UVA Center for Survey Research.

The study has been published in the Journal of the American College of Radiology. The paper was authored by Guterbock, Cohn, Deborah L. Rexrode, Casey M. Eggleston, Melissa Dean-McKinney, Wendy M. Novicoff, William A. Knaus and Jennifer A. Harvey from UVA, along with Martin J. Yaffe from Sunnybrook Research Institute in Toronto.

For more information: www.news.virginia.edu

Related Content

Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer

Fatty tissue and breast density may be considered in the context of many factors that affect the occurrence and detection of breast cancer. Permission to publish provided by DenseBreast-info.org

Feature | Breast Imaging | April 18, 2019 | By Greg Freiherr
When planning a screening program to detect the early signs of breast cancer, age is a major consideration.
iCAD Appoints Stacey Stevens as President
News | Radiology Business | April 16, 2019
iCAD Inc. recently announced that Stacey Stevens has been named president. As president, Stevens will have expanded...
compressed breast during mammography.
360 Photos | 360 View Photos | April 16, 2019
A 360 view of a simulated breast compression for a...
Check-Cap Initiates U.S. Pilot Study of C-Scan for Colorectal Cancer Screening
News | Colonoscopy Systems | April 15, 2019
Check-Cap Ltd. has initiated its U.S. pilot study of the C-Scan system for prevention of colorectal cancer through...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used “Deep Learning,“ a form of machine learning, which is a type of artificial intelligence. Graphic courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Artificial Intelligence | April 12, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
This image depicts ABUS images with QVCAD results

This image depicts ABUS images with QVCAD results.

Feature | Breast Imaging | April 12, 2019
Imaging Technology News spoke with Bob Foley, vice president of sales and marketing of QView Medical, Inc.,
Deep Lens Closes Series A Financing for Digital AI Pathology Platform
News | Digital Pathology | April 09, 2019
Digital pathology company Deep Lens Inc. announced the closing of a $14 million Series A financing that will further...
Uterine Fibroid Embolization Safer and as Effective as Surgical Treatment
News | Interventional Radiology | April 05, 2019
Uterine fibroid embolization (UFE) effectively treats uterine fibroids with fewer post-procedure complications compared...