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Updated: Jun 6, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Using mammographic density to predict breast cancer risk: dense area or percentage dense area
Jennifer Stone1, Jane Ding, Ruth Ml Warren
1Centre for Molecular, Environmental, Genetic and Analytical (MEGA) Epidemiology, The University of Melbourne, School of Population Health, Victoria, 3010, Australia. stonej@unimelb.edu.au
Introduction:
Mammographic density (MD) is one of the strongest risk factors for breast cancer. It is not clear whether this association is best expressed in terms of absolute dense area or percentage dense area (PDA).
Methods:
We measured MD, including nondense area (here a surrogate for weight), in the mediolateral oblique (MLO) mammogram using a computer-assisted thresholding technique for 634 cases and 1,880 age-matched controls from the Cambridge and Norwich Breast Screening programs. Conditional logistic regression was used to estimate the risk of breast cancer, and fits of the models were compared using likelihood ratio tests and the Bayesian information criteria (BIC). All P values were two-sided.
Results:
Square-root dense area was the best single predictor (for example, χ₁² = 53.2 versus 44.4 for PDA). Addition of PDA and/or square-root nondense area did not improve the fit (both P > 0.3). Addition of nondense area improved the fit of the model with PDA (χ₁² = 11.6; P < 0.001). According to the BIC, the PDA and nondense area model did not provide a better fit than the dense area alone model. The fitted values of the two models were highly correlated (r = 0.97). When a measure of body size is included with PDA, the predicted risk is almost identical to that from fitting dense area alone.
Conclusions:
As a single parameter, dense area provides more information than PDA on breast cancer risk.
