6th International Workshop on Breast Densitometry and Breast ...

P21
6 th <strong>International</strong> <strong>Workshop</strong> on Breast Densitometry
and Breast Cancer Risk Assessment
MAMMOGRAPHIC DENSITY PHENOTYPES AND RISK OF BREAST CANCER: A
META-ANALYSIS
Andreas Pettersson 1 , Rebecca E. Graff 1 , Laura Baglietto 2, 3 , Marije F. Bakker 4 , Norman Boyd 5 , KeeSeng
Chia 6 , Kamila Czene 7 , Isabel dos Santos Silva 8 , Louise Eriksson 7 , Graham G. Giles 2, 3, 9 , Per Hall 7 , Mikael
Hartman 6, 7, 9 , John L. Hopper 3 , Kavitha Krishnan 2, 3 , Jingmei Li 11 , Qing Li 5 , Gertraud Maskarinec 12 ,
Valerie McCormack 13 , Ian Pagano 12 , Bernard A. Rosner 14, 15 , Christopher Scott 16 , Jennifer Stone 3 , Giske
Ursin 17, 18 , Celine Vachon 16 , Carla H. van Gils 4 , Chia Siong Wong 6 1, 14
, Rulla M. Tamimi
1) Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; 2) Cancer
Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia; 3) Centre for Molecular,
Environmental, Genetic and Analytical Epidemiology, The University of Melbourne, Australia; 4) Julius
Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands;
5) Campbell Family Institute for Breast Cancer Research, Ontario Cancer Institute, Toronto, Ontario,
Canada; 6) Saw Swee Hock School of Public Health, National University of Singapore, National
University Health System, Singapore; 7) Department of Medical Epidemiology and Biostatistics,
Karolinska Institutet, Stockholm, Sweden; 8) Department of Non-Communicable Disease Epidemiology,
London School of Hygiene and Tropical Medicine, London, United Kingdom; 9) Department of
Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; 10) Department of
Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 11) Human
Genetics Division, Genome Institute of Singapore, Singapore; 12) Department of Epidemiology, University
of Hawaii Cancer Center, Honolulu, HI, USA; 13) Section of Environment and Radiation, <strong>International</strong>
Agency for Research on Cancer, Lyon, France; 14) Channing Division of Network Medicine, Department
of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA;
15) Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; 16) Department of
Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA; 17) Department of
Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway; 18) Department of
Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Background: Fibroglandular breast tissue appears dense on mammogram; fat appears nondense. We
investigated whether absolute or percent dense area more strongly predicts breast cancer risk, and whether
absolute nondense area is independently associated with risk.
Methods: We conducted a meta-analysis of 13 case-control studies, each having provided parameter
estimates and standard errors from logistic regressions for associations between one standard deviation
(SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects
models to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs).
ABSTRACTS
Results: Among premenopausal women (1,776 cases; 2,834 controls), summary ORs for one SD
increments were 1.37 (95% CI: 1.29-1.47) for absolute dense area, 0.78 (95% CI: 0.71-0.86) for absolute
nondense area, and 1.52 (95% CI: 1.39-1.66) for percent dense area when pooling estimates adjusted for
age, BMI and parity. Corresponding ORs among postmenopausal women (6,643 cases; 11,187 controls)
were 1.38 (95% CI: 1.31-1.44), 0.79 (95% CI: 0.73-0.85), and 1.53 (95% CI: 1.44-1.64). After additional
adjustment for absolute dense area, associations between absolute nondense area and breast cancer
became attenuated or null in several studies and summary ORs became 0.82 (95% CI: 0.71-0.94; P-
heterogeneity: 0.02) for premenopausal and 0.85 (95% CI: 0.75-0.96; P-heterogeneity: