Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk.
Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer.



We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus , and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus , respectively. All measures were Box-Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC).



Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6 , respectively) . For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus , respectively (all P  < 0.001). After adjusting for Altocumulus and Cirrocumulus , Cumulus was not significant ( P  > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64-2.14] and AUC = 0.68 (0.65-0.71).



The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research.
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Tuong L
TL
Nguyen
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Ye K
YK
Aung
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Christopher F
CF
Evans
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Gillian S
GS
Dite
Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia.
Jennifer
J
Stone
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Robert J
RJ
MacInnis
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
James G
JG
Dowty
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Adrian
A
Bickerstaffe
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Kelly
K
Aujard
Program in Genetics and Genomic Biology, Hospital for Sick Children, Toronto, ON, Canada.
Johanna M
JM
Rommens
Department of Family Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea and.
Yun-Mi
YM
Song
Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.|Institute of Health and Environment, Seoul National University, Seoul, Korea.
Joohon
J
Sung
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Mark A
MA
Jenkins
Department of Pathology, University of Melbourne, VIC Australia.
Melissa C
MC
Southey
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.|Cancer Council Victoria, Melbourne, VIC, Australia.
Graham G
GG
Giles
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.
Carmel
C
Apicella
Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.|Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.|Institute of Health and Environment, Seoul National University, Seoul, Korea.
John L
JL
Hopper
D000328:Adult; D001315:Australia; D015992:Body Mass Index; D001940:Breast / Q000000981:diagnostic imaging*; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016022:Case-Control Studies; D055088:Early Detection of Cancer; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography; D008875:Middle Aged; D012372:ROC Curve; D012042:Registries; D012307:Risk Factors; D012984:Software
Australian women; Breast cancer; case-control study; mammographic density; mammography