Abstract
BackgroundCase–control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers.MethodWe conducted a nested case–control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC).ResultsFor interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85–2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07).ConclusionThe amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.
Highlights
Case–control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds
For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85–2.92); all ΔBIC > 14), and the association with body mass index (BMI) was independent of age at mammogram
Percent dense area measured by Cumulus, Altocumulus and Cirrocumulus was greater for cases with interval breast cancers compared with (1) their controls, and (2) cases with screen-detected breast cancers
Summary
Case–control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds We asked if this applied to interval and/or screen-detected cancers. Mammographic images can be used for more than identifying existing breast tumours – they contain information that predicts both (1) the risk of future breast cancers and (2) the likelihood of existing tumours being missed due to masking [1]. This has important implications for future breast cancer control because of the widespread use of mammography screening. Interval cancers are in general more aggressive [6,7,8,9,10,11], so it is important to understand if the relationship between mammographic density measures and breast cancer risk differs by mode of detection [12]
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