title: Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods.
abstract: BACKGROUND
  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.
authors.forename: Amanda
authors.initials: A
authors.lastname: Eng
authors.forename: Zoe
authors.initials: Z
authors.lastname: Gallant
authors.forename: John
authors.initials: J
authors.lastname: Shepherd
authors.forename: Valerie
authors.initials: V
authors.lastname: McCormack
authors.forename: Jingmei
authors.initials: J
authors.lastname: Li
authors.forename: Mitch
authors.initials: M
authors.lastname: Dowsett
authors.forename: Sarah
authors.initials: S
authors.lastname: Vinnicombe
authors.forename: Steve
authors.initials: S
authors.lastname: Allen
authors.forename: Isabel
authors.initials: I
authors.lastname: dos-Santos-Silva
mesh_terms: D000368:Aged; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted; D012307:Risk Factors