Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study.
Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-detected include those that predict masking. Our aim was to investigate the associations between mammographic measures and (1) inherent risk, and (2) masking.



We conducted a case-control study nested within the Melbourne collaborative cohort study of 244 screen-detected cases (192 small tumors (<2 cm)) matched to 700 controls and 148 interval cases (76 small tumors) matched to 446 controls. Dense area (DA), percent dense area (PDA), and non-dense area (NDA) were measured using the Cumulus software. Conditional and unconditional logistic regression were applied as appropriate to estimate the odds per adjusted standard deviation (OPERA) adjusted for age and body mass index (BMI), allowing for the association with BMI to be a function of age at diagnosis. Tests of fit were performed using the Bayesian information criterion (BIC) and the area under the receiver operating characteristic curve.



For screen-detected cancer, the association with BMI had a marginally significant dependence on age at diagnosis, and after adjustment both DA and PDA were associated with risk (OPERA approximately 1.2) and gave a similar fit. NDA was not associated with risk. For interval cancer, the BMI risk association was not dependent on age at diagnosis and the best fitting model was PDA alone (OPERA = 2.24, 95 % confidence interval 1.75, 2.86). Prediction of interval versus screen-detected cancer was best achieved by PDA alone (OPERA = 1.76, 95 % confidence interval 1.39, 2.22) with no association with BMI. When the analysis was restricted to small tumors to reduce the influence of tumor growth, we obtained similar results.



Inherent breast cancer risk is predicted by BMI and DA or PDA, but not NDA. Masking is predicted by PDA, and not by BMI. Understanding risk and masking could help tailor mammographic screening.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.
Kavitha
K
Krishnan
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.|Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.|Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France.|Gustave Roussy, F-94805, Villejuif, France.
Laura
L
Baglietto
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.
Carmel
C
Apicella
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.|Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Crawley, Australia.
Jennifer
J
Stone
Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia.
Melissa C
MC
Southey
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.|Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.
Dallas R
DR
English
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.|Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.|Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Graham G
GG
Giles
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia. j.hopper@unimelb.edu.au.|Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea. j.hopper@unimelb.edu.au.|Institute of Health and Environment, Seoul National University, Seoul, Korea. j.hopper@unimelb.edu.au.
John L
JL
Hopper
D000328:Adult; D000368:Aged; D015992:Body Mass Index; D000071060:Breast Density; D001943:Breast Neoplasms / Q000453:epidemiology* / Q000473:pathology*; D016022:Case-Control Studies; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D015994:Incidence; D008327:Mammography / Q000379:methods; D008875:Middle Aged; D012306:Risk; D012307:Risk Factors; D047368:Tumor Burden
Breast cancer; Detection mode; Mammographic density; Tumor size