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Erschienen in:
07.03.2018 | Epidemiology
Mammographic non-dense area and breast cancer risk in postmenopausal women: a causal inference approach in a case–control study
Erschienen in: Breast Cancer Research and Treatment | Ausgabe 1/2018
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Purpose
The association between high mammographic density (MD) and elevated breast cancer risk is well established. However, the role of absolute non-dense area remains unclear. We estimated the effect of the mammographic non-dense area and other density parameters on the risk of breast cancer.
Methods
This study utilizes data from a population-based case–control study conducted in Greater Vancouver, British Columbia, with 477 female postmenopausal breast cancer cases and 588 female postmenopausal controls. MD measures were determined from digitized screening mammograms using computer-assisted software (Cumulus). Marginal odds ratios were estimated by inverse-probability weighting using a causal diagram for confounder selection. Akaike information criteria and receiver operating characteristic curves were used to assess the goodness of fit and predictive power of unconditional logistic models containing MD parameters.
Results
The risk of breast cancer is 60% lower for the highest quartile compared to the lowest quartile of mammographic non-dense area (marginal OR 0.40, 95% CI 0.26–0.61, p-trend < 0.001). The cancer risk almost doubles for the highest quartile compared to the lowest quartile of dense area (marginal OR 1.81, 95% CI 1.19–2.43, p-trend < 0.001). For the highest quartile of percent density, breast cancer risk was more than three times higher than for the lowest quartile (marginal OR 3.15, 95% CI 1.90–4.40, p-trend < 0.001). No difference was seen in predictive accuracy between models using percent density alone, dense area alone, or non-dense area plus dense area.
Conclusions
In this study, non-dense area is an independent risk factor after adjustment for dense area and other covariates, inversely related with the risk of breast cancer. However, non-dense area does not improve prediction over that offered by percent density or dense area alone.