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 percent dense area (PDA).

We measured MD, including non-dense area (here a surrogate for weight), in the medio-lateral oblique (MLO) mammogram using a computer-assisted thresholding technique for 634 cases and 1880 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.

Square-root dense area was the best single predictor (e.g. chi1^2 = 53.2 versus 44.4 for PDA). Addition of PDA and/or square-root non-dense area did not improve the fit (both P >0.3). Addition of non-dense area improved the fit of the model with PDA (chi1^2 = 11.6; P <0.001). According to the BIC, the PDA and non-dense 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.

As a single parameter, dense area provides more information on breast cancer risk than PDA.

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