Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds |
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| Authors: | Tuong L. Nguyen Ye K. Aung Shuai Li Nhut Ho Trinh Christopher F. Evans Laura Baglietto Kavitha Krishnan Gillian S. Dite Jennifer Stone Dallas R. English Yun-Mi Song Joohon Sung Mark A. Jenkins Melissa C. Southey Graham G. Giles John L. Hopper |
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| Affiliation: | 1.Centre for Epidemiology and Biostatistics,The University of Melbourne,Carlton,Australia;2.Cancer Epidemiology and Intelligence Division,Cancer Council Victoria,Melbourne,Australia;3.Curtin UWA Centre for Genetic Origins of Health and Disease,Curtin University and the University of Western Australia,Perth,Australia;4.Department of Family Medicine, Samsung Medical Center,Sungkyunkwan University School of Medicine,Gangnamgu,South Korea;5.Department of Epidemiology School of Public Health,Seoul National University,Seoul,Korea;6.Department of Pathology,University of Melbourne,Carlton,Australia;7.Institute of Health and Environment,Seoul National University,Seoul,Korea;8.Department of Clinical and Experimental Medicine,University of Pisa,?Pisa,Italy;9.Precision Medicine, School of Clinical Sciences at Monash Health,Monash University,Clayton,Australia |
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| 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. |
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