Enhancement of mammographic density measures in breast cancer risk prediction.
BACKGROUND
Mammographic density is a strong risk factor for breast cancer.


METHODS
We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density.


RESULTS
From analysis of data from a Swedish case-control study (1,286 breast cancer cases and 1,391 control subjects, ages 50-75 years), we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk [per SD: OR = 0.82; 95% confidence interval (CI), 0.75-0.88; P = 6 × 10(-7)] after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The area under curve (AUC) changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2,655 breast cancer cases (combined P = 0.0002).


CONCLUSIONS
MIP is a marker of volumetric density that can be used to complement area PD in mammographic density studies and breast cancer risk assessment.


CONCLUSIONS
Inclusion of MIP in risk models should be considered for studies using area PD from analog films.
Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;
Abbas
A
Cheddad
Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;
Kamila
K
Czene
Department of Radiology and Biomedical Imaging, UCSF School of Medicine, University of California, San Francisco, California; and.
John A
JA
Shepherd
Human Genetics, Genome Institute of Singapore, Singapore.
Jingmei
J
Li
Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;
Per
P
Hall
Authors' Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Keith.Humphreys@ki.se.
Keith
K
Humphreys
D000368:Aged; D019540:Area Under Curve; D000071060:Breast Density; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics; D004268:DNA-Binding Proteins / Q000235:genetics; D005260:Female; D005838:Genotype; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D010369:Pectoralis Muscles / Q000000981:diagnostic imaging*; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted; D012307:Risk Factors; D014157:Transcription Factors / Q000235:genetics
D004268:DNA-Binding Proteins; D014157:Transcription Factors; C475725:ZNF365 protein, human