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6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment P27 ASSOCIATION BETWEEN AUTOMATED, VOLUMETRIC BREAST DENSITY MEASURES AND BREAST CANCER IN A LARGE SCREENING POPULATION M.G.J. Kallenberg 1,2 , K. Holland 1 , J.O.P. Wanders 3 , G.H. van Gils 3 , and N. Karssemeijer 1 1 Radboud University Nijmegen Medical Centre, Nijmegen, the Neterlands; 2 Matakina Technology, Wellington, New Zealand, michiel.kallenberg@matakina.com; 3 Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, the Netherlands PURPOSE: The association between breast density and breast cancer risk has been established with semi-automated, area based measurement of breast density. In this work we investigate the performance of a fully automated, volumetric method to predict breast cancer in a large screening population. MATERIALS AND METHOD: The data that was used in this work comprised 107,488 digital screening exams, performed in 2003-2012, from 53,975 women in the Dutch Breast Cancer Screening Program, with a mean age of 59.2 years. The set contained 730 biopsy proven malignant breast cancer cases that were diagnosed through the screening program (screen-detected). Interval tumors (diagnosed in between screening rounds) have not yet been identified for this screening period. For 363 cases only mammograms in which cancers were detected were available, while for 367 cases we also had prior screening mammograms. For the latter cases we selected the oldest mammographic examination available. The average time between the oldest mammographic examination and diagnosis was 3.5 years. All images were recorded on a Hologic Selenia FFDM system, using standard clinical settings. For each mammographic image we assessed volumetric breast density with commercially available software (Volpara, Matakina Technology, Wellington, New Zealand, version 1.4.0). To obtain one density estimate per exam we averaged over the MLO and CC view and over the left and right breast. When a case contained pathology we averaged only over the non affected side. We examined the relationship between volume of fibroglandular tissue and the risk of breast cancer by calculating odds ratios and their 95% confidence intervals. Exams were categorized into four groups, in such a way that the group sizes equal the group sizes one would obtain with Volpara density grade (VDG) scoring, which is a four point scale analog to BI-RADS density scores. We used logistic regression to adjust for age at mammography and breast volume, which served as a surrogate marker for BMI. RESULTS: After adjustment for age and breast volume, dense volume was significantly higher in cases than controls (p
P28 6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry and Breast Cancer Risk Assessment LONGITUDINAL TRACKING WITH TIME OF MAMMOGRAPHIC DENSITY MEASURES THAT PREDICT BREAST CANCER Kavitha Krishnan 1,2 , Laura Baglietto 1,2 , Jennifer Stone 2 , Carmel Apicella 2 , Dallas R English 1,2 , Gianluca Severi 1,2 , Graham G Giles 1,2,3 , John Hopper 2 1 Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, AUSTRALIA, 2 Centre for Molecular, Environmental, Genetic and Analytical epidemiology, University of Melbourne, AUSTRALIA, 3 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, AUSTRALIA Background: Mammographic density adjusted for age and body mass index (BMI) is an established risk factor for breast cancer. If density for age and BMI tracks strongly with time from a young age then it could be possible to identify young women at high risk of breast cancer and try to offer them more effective screening regimes. This might increase early detection of breast cancer and hence reduce mortality due to breast cancer. ABSTRACTS Methods: Combining both the Melbourne Collaborative Cohort Study (MCCS) and the Australian Breast Cancer Family Study (ABCFS), we identified 1,064 women who had an average of 4 mammograms each between the ages of 24-83 years (N=4,723 mammograms; range 1-14 mammograms per woman). Mammographic density was measured with the software Cumulus and transformed to approximate normality using the Box-Cox power formula. BMI at each mammogram was assigned using measures available at study entry and at two follow-ups for the MCCS and one follow-up for the ABCFS based on the proximity of the date at which the mammogram was taken and the attendance dates of the studies. Transformed dense area adjusted for age and BMI was fitted using mixed models with a random intercept, overall and separately for mammograms in the premenopausal age group 24-49 and postmenopausal age group 50-83, with no correlation between measurements, exponential correlation between measurements and random slope. Estimates of the correlations between dense areas between different ages were estimated from the best fit model identified using the log restricted-likelihood. Results: Dense area was transformed to the power of 0.2. The overall best fit model was mean dense area decreasing quadratically with age and linearly with BMI with an exponential correlation structure. The correlations in adjusted dense area measures 2, 4, 6, 8 and 10 years apart were 0.93, 0.90, 0.89, 0.88 and 0.88, respectively. For mammograms in the premenopausal age group 24-49, the mean dense area decreased quadratically with age but there was no significant association with BMI. The correlations in adjusted dense area measures 2, 4, 6, 8 and 10 years apart were slightly lower at 0.85, 0.80, 0.79, 0.79 and 0.78, respectively. The model for the postmenopausal age group was similar to the overall model and the correlations were also slightly higher than the overall model, being 0.94, 0.91, 0.90, 0.89 and 0.89 at 2, 4, 6, 8 and 10 years apart, respectively. Conclusion: Mammographic dense area measurements that predict breast cancer risk are highly correlated over 10 years regardless of menopausal status. 63
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