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.2013;15(5):R80.
doi: 10.1186/bcr3474.

AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes

AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes

Carolyn Nickson et al. Breast Cancer Res.2013.

Abstract

Introduction: While Cumulus - a semi-automated method for measuring breast density - is utilised extensively in research, it is labour-intensive and unsuitable for screening programmes that require an efficient and valid measure on which to base screening recommendations. We develop an automated method to measure breast density (AutoDensity) and compare it to Cumulus in terms of association with breast cancer risk and breast cancer screening outcomes.

Methods: AutoDensity automatically identifies the breast area in the mammogram and classifies breast density in a similar way to Cumulus, through a fast, stand-alone Windows or Linux program. Our sample comprised 985 women with screen-detected cancers, 367 women with interval cancers and 4,975 controls (women who did not have cancer), sampled from first and subsequent screening rounds of a film mammography screening programme. To test the validity of AutoDensity, we compared the effect estimates using AutoDensity with those using Cumulus from logistic regression models that tested the association between breast density and breast cancer risk, risk of small and large screen-detected cancers and interval cancers, and screening programme sensitivity (the proportion of cancers that are screen-detected). As a secondary analysis, we report on correlation between AutoDensity and Cumulus measures.

Results: AutoDensity performed similarly to Cumulus in all associations tested. For example, using AutoDensity, the odds ratios for women in the highest decile of breast density compared to women in the lowest quintile for invasive breast cancer, interval cancers, large and small screen-detected cancers were 3.2 (95% CI 2.5 to 4.1), 4.7 (95% CI 3.0 to 7.4), 6.4 (95% CI 3.7 to 11.1) and 2.2 (95% CI 1.6 to 3.0) respectively. For Cumulus the corresponding odds ratios were: 2.4 (95% CI 1.9 to 3.1), 4.1 (95% CI 2.6 to 6.3), 6.6 (95% CI 3.7 to 11.7) and 1.3 (95% CI 0.9 to 1.8). Correlation between Cumulus and AutoDensity measures was 0.63 (P < 0.001).

Conclusions: Based on the similarity of the effect estimates for AutoDensity and Cumulus inmodels of breast density and breast cancer risk and screening outcomes, we conclude that AutoDensity is a valid automated method for measuring breast density from digitised film mammograms.

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Figures

Figure 1
Figure 1
Examples of theAutoDensitybreast density segmentation process on three cranio-caudal-view digitised film mammograms. Examples shown are from three women with different breast densities, from each of the lowest quintile (Q1), third quintile (Q3) and upper decile (D10) of the population distribution of dense area. For each woman, the breast density segmentation process is illustrated using(A) the input mammogram,(B) the dense area outline, and(C) the dense area mask.
Figure 2
Figure 2
Distribution of tumour size and mode of detection according to dense area quintiles measured using (A)Cumulusand (B)AutoDensity. Screen-detected and interval cancers (marked as dark and light bars) are shown separately but stacked to indicate the size distribution of all cancers detected in each breast density quintile as well as the relative representation of screen-detected and interval cancers in this distribution. The dashed line represents the expected tumour size distribution within each breast density group if the distribution of tumour size did not vary according to breast density.
Figure 3
Figure 3
Receiver operating characteristic (ROC) curves and area under the curve (AUC) values to assess the discriminatory performance ofCumulusandAutoDensitydense area. ROC curves and AUC values are shown for various outcomes, including(A) all cancers versus controls,(B) small screen-detected cancers versus controls,(C) large screen-detected cancers versus controls,(D) interval cancers versus controls,(E) interval cancers versus screen-detected cancers for first round screening, and(F) interval cancers versus screen-detected cancers for subsequent round screening.Cumulus values are shown in black,AutoDensity values are shown in grey. The dashed line represents no predictive value.
Figure 4
Figure 4
Multivariate logistic regression of cancer risk and screening outcomes according toCumulusandAutoDensity. Breast density was measured as dense area and categorised into quintile-decile groups. Regression models were adjusted for age, hormone therapy use, family history of breast cancer, symptoms and screening round.
Figure 5
Figure 5
Distribution and variation betweenCumulusandAutoDensity. (A) Scatter plot of screened population percentiles of dense area for a 20% random sample of the study group.(B) Bland-Altman plot of agreement betweenCumulus andAutoDensity dense area for a 20% random sample of the study group. The x-axis shows the mean value of theCumulus andAutoDensity measurements for each image, on a log scale.(C) Quantile-quantile plot of dense area percentiles ofCumulus against percentiles ofAutoDensity (a deviation from the diagonal indicates a difference in distributions).(D) Cross-classification of quintile-decile groups (%).
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