Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density

Background Mammograms contain information that predicts breast cancer risk. We recently discovered two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). It is not known whether these measures improve risk prediction when fitted together, and with an established measure of mammographic density (Cumulus). Methods We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1,197 matched controls; and 354 younger-diagnosis cases and 944 frequency-matched controls. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We reported risk gradients as change in odds ratio per standard deviation of controls after adjusting for age and body mass index (OPERA). For models involving multiple measures, we calculated the OPERA equivalent to the area under the receiver operating characteristic curve. Results For interval, screen-detected and younger-diagnosis cancer, the best fitting models (OPERAs [95% confidence intervals]) were: Cumulus (1.81 [1.41 to 2.31]) and Cirrus (1.7 [1.38 to 2.14]); Cirrus (1.49 [1.32 to 1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27 to 1.68]), respectively. Their OPERA equivalents were: 2.35, 1.58, and 2.28, respectively. Conclusions Our mammogram-based measures improved risk prediction beyond and, except for interval cancers, negated the influence of conventional mammographic density. Combined, these new mammogram-based risk measures are at least as accurate as the current polygenetic risk scores (OPERA ~ 1.6) in predicting, on a population basis, women who will be diagnosed with breast cancer.