Abstract
IntroductionMammographic density is well-established as a risk factor for breast cancer, however, adjustment for age and body mass index (BMI) is vital to its clinical interpretation when assessing individual risk. In this paper we develop a model to adjust mammographic density for age and BMI and show how this adjusted mammographic density measure might be used with existing risk prediction models to identify high-risk women more precisely.MethodsWe explored the association between age, BMI, visually assessed percent dense area and breast cancer risk in a nested case-control study of women from the placebo arm of the International Breast Cancer Intervention Study I (72 cases, 486 controls). Linear regression was used to adjust mammographic density for age and BMI. This adjusted measure was evaluated in a multivariable logistic regression model that included the Tyrer-Cuzick (TC) risk score, which is based on classical breast cancer risk factors.ResultsPercent dense area adjusted for age and BMI (the density residual) was a stronger measure of breast cancer risk than unadjusted percent dense area (odds ratio per standard deviation 1.55 versus 1.38; area under the curve (AUC) 0.62 versus 0.59). Furthermore, in this population at increased risk of breast cancer, the density residual added information beyond that obtained from the TC model alone, with the AUC for the model containing both TC risk and density residual being 0.62 compared to 0.51 for the model containing TC risk alone (P =0.002).Approximately 16% of controls and 19% of cases moved into the highest risk group (8% or more absolute risk of developing breast cancer within 10 years) when the density residual was taken into account. The net reclassification index was +15.7%.ConclusionsIn women at high risk of breast cancer, adjusting percent mammographic density for age and BMI provides additional predictive information to the TC risk score, which already incorporates BMI, age, family history and other classic breast cancer risk factors. Furthermore, simple selection criteria can be developed using mammographic density, age and BMI to identify women at increased risk in a clinical setting.Clinical trial registration numberhttp://www.controlled-trials.com/ISRCTN91879928 (Registered: 1 June 2006).Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0451-5) contains supplementary material, which is available to authorized users.
Highlights
Mammographic density is well-established as a risk factor for breast cancer, adjustment for age and body mass index (BMI) is vital to its clinical interpretation when assessing individual risk
Aside from the challenges involved in measuring mammographic density, the issue of how to utilise mammographic density information to estimate breast cancer risk is complicated by the fact that there is confounding between percent mammographic density, body mass index (BMI) [2] and age [3], and possibly other breast cancer risk factors as well
We use data on a subset of women from the placebo arm of the International Breast Cancer Intervention Study 1 (IBIS-1) [12,13] to develop a model to adjust mammographic density for age and BMI and show how this adjusted mammographic density measure might be used with existing risk prediction models to identify high-risk women more precisely
Summary
Mammographic density is well-established as a risk factor for breast cancer, adjustment for age and body mass index (BMI) is vital to its clinical interpretation when assessing individual risk. In many health care systems access to breast cancer risk-reducing interventions is contingent upon having a high breast cancer risk as assessed by one of these models It is vital, to further develop these models to incorporate mammographic density so that when we are making such, sometimes life changing, decisions we are taking into account all available information. We use data on a subset of women from the placebo arm of the International Breast Cancer Intervention Study 1 (IBIS-1) [12,13] to develop a model to adjust mammographic density (percent dense area) for age and BMI and show how this adjusted mammographic density measure might be used with existing risk prediction models to identify high-risk women more precisely
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.