Mammographic density defined by higher than conventional brightness threshold better predicts breast cancer risk for full-field digital mammograms.

Tuong Linh Nguyen,Choi Yoon-Ho,Ye Kyaw Aung,Christopher Francis Evans,Mark Anthony Jenkins,John Llewelyn Hopper,Joohon Sung,Yun-Mi Song

doi:10.1186/s13058-015-0654-4

Abstract

IntroductionWhen measured using the computer-assisted method CUMULUS, mammographic density adjusted for age and body mass index predicts breast cancer risk. We asked if new mammographic density measures defined by higher brightness thresholds gave better risk predictions.MethodsThe Korean Breast Cancer Study included 213 women diagnosed with invasive breast cancer and 630 controls matched for age at full-field digital mammogram and menopausal status. Mammographic density was measured using CUMULUS at the conventional threshold (Cumulus), and in effect at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box-Cox transformed and adjusted for age, body mass index, menopausal status and machine. We used conditional logistic regression to estimate the change in Odds PER standard deviation of transformed and Adjusted density measures (OPERA). The area under the receiver operating characteristic curve (AUC) was estimated.ResultsCorresponding Altocumulus and Cirrocumulus density measures were correlated with Cumulus measures (r approximately 0.8 and 0.6, respectively). Altocumulus and Cirrocumulus measures were on average 25 % and 80 % less, respectively, than the Cumulus measure. For dense area, the OPERA was 1.18 (95 % confidence interval: 1.01−1.39, P = 0.03) for Cumulus; 1.36 (1.15−1.62, P < 0.001) for Altocumulus; and 1.23 (1.04−1.45, P = 0.01) for Cirrocumulus. After fitting the Altocumulus measure, the Cumulus measure was no longer associated with risk. After fitting the Cumulus measure, the Altocumulus measure was still associated with risk (P = 0.001). The AUCs for dense area was 0.59 for the Altocumulus measure, greater than 0.55 and 0.57 for the Cumulus and Cirrocumulus measures, respectively (P = 0.001). Similar results were found for percentage dense area measures.ConclusionsAltocumulus measures perform better than Cumulus measures in predicting breast cancer risk, and Cumulus measures are confounded by Altocumulus measures. The mammographically bright regions might be more aetiologically important for breast cancer, with implications for biological, molecular, genetic and epidemiological research and clinical translation.

Highlights

When measured using the computer-assisted method CUMULUS, mammographic density adjusted for age and body mass index predicts breast cancer risk
We found that the cube root transformation was appropriate for the Cumulus and Altocumulus dense area measures and a logarithmic transformation was appropriate for the Cirrocumulus measure
We evaluated the association between mammographic density and breast cancer risk by fitting conditional logistic regression models, adjusting for machine, with the mammographic density measures as both continuous and categorical variables

Summary

Introduction

When measured using the computer-assisted method CUMULUS, mammographic density adjusted for age and body mass index predicts breast cancer risk. The incidence and prevalence of breast cancer has been lower in Asian countries than Western countries [1, 2]. This is changing rapidly with economic development over the past few decades and is expected to increase over the 20 years [3, 4]. Mammographic density is one of strongest risk factors for breast cancer [5, 6] It has been defined by the white or bright, as distinct from dark, areas on a mammogram. A well-established measurement uses the computer-assisted thresholding method CUMULUS, in which the observer visually selects a pixel threshold to define the dense areas for each particular mammogram [7,8,9]

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