Three-quarters of adults around the world are unaware that dense breast tissue increases a woman's cancer risk, according to a survey performed by GE Healthcare.
The company conducted its "Value of Knowing" survey during May and June 2014 across 10 countries -- Australia, Brazil, China, India, Indonesia, Japan, Russia, South Korea, the U.K., and the U.S. -- with 1,000 adult respondents in each market participating. The survey consisted of a 15-minute online interview.
The research found that only one of five people has seen, heard, or read about dense breast tissue in the past six months, and that more than half of survey participants could not name six common symptoms of breast cancer. Awareness of the connection between dense breast tissue and increased cancer risk varied by country.
Breast density risk awareness by country:
- Russia: 60%
- Indonesia: 58%
- China: 34%
- U.S.: 19%
- Australia: 13%
- U.K.: 9%
- Japan: 2%
"The results of this research highlight an opportunity to further encourage awareness of dense breast tissue and empower women to take an active role in their breast health," Dr. Jessie Jacob, GE's chief medical officer of breast health, said in a statement released by the company. "Although the importance of breast cancer screening is well-known, this survey suggests that dense breast tissue, a relevant risk factor, is not widely understood."
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
