Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13058-014-0439-1
Title: Digital mammographic density and breast cancer risk: A case-control study of six alternative density assessment methods
Authors: Eng, A
Gallant, Z
Shepherd, J
McCormack, V
Li, J 
Dowsett, M
Vinnicombe, S
Allen, S
dos-Santos-Silva, I
Keywords: adult
age
aged
Article
body mass
breast cancer
breast density
cancer risk
controlled study
digital mammography
female
human
imaging software
intermethod comparison
major clinical study
middle aged
postmenopause
risk factor
single energy x ray absorptiometry
X ray spectrometry
abnormalities
breast tumor
case control study
comparative study
computer assisted diagnosis
mammary gland
radiography
Aged
Breast Neoplasms
Case-Control Studies
Female
Humans
Mammary Glands, Human
Middle Aged
Radiographic Image Interpretation, Computer-Assisted
Risk Factors
Issue Date: 2014
Citation: Eng, A, Gallant, Z, Shepherd, J, McCormack, V, Li, J, Dowsett, M, Vinnicombe, S, Allen, S, dos-Santos-Silva, I (2014). Digital mammographic density and breast cancer risk: A case-control study of six alternative density assessment methods. Breast Cancer Research 16 (5) : 439. ScholarBank@NUS Repository. https://doi.org/10.1186/s13058-014-0439-1
Rights: Attribution 4.0 International
Abstract: Introduction: Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM).Methods: The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables.Results: Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65).Conclusions: Fully-automated methods are valid alternatives to the labour-intensive " gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments. © 2014 Eng et al.; licensee BioMed Central Ltd.
Source Title: Breast Cancer Research
URI: https://scholarbank.nus.edu.sg/handle/10635/183658
ISSN: 14655411
DOI: 10.1186/s13058-014-0439-1
Rights: Attribution 4.0 International
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