title: A novel automated mammographic density measure and breast cancer risk.
abstract: BACKGROUND
  Mammographic breast density is a strong breast cancer risk factor but is not used in the clinical setting, partly because of a lack of standardization and automation. We developed an automated and objective measurement of the grayscale value variation within a mammogram, evaluated its association with breast cancer, and compared its performance with that of percent density (PD).
  
  
  METHODS
  Three clinic-based studies were included: a case-cohort study of 217 breast cancer case subjects and 2094 non-case subjects and two case-control studies comprising 928 case subjects and 1039 control subjects and 246 case subjects and 516 control subjects, respectively. Percent density was estimated from digitized mammograms using the computer-assisted Cumulus thresholding program, and variation was estimated from an automated algorithm. We estimated hazards ratios (HRs), odds ratios (ORs), the area under the receiver operating characteristic curve (AUC), and 95% confidence intervals (CIs) using Cox proportional hazards models for the cohort and logistic regression for case-control studies, with adjustment for age and body mass index. We performed a meta-analysis using random study effects to obtain pooled estimates of the associations between the two mammographic measures and breast cancer. All statistical tests were two-sided.
  
  
  RESULTS
  The variation measure was statistically significantly associated with the risk of breast cancer in all three studies (highest vs lowest quartile: HR = 2.0 [95% CI = 1.3 to 3.1]; OR = 2.7 [95% CI = 2.1 to 3.6]; OR = 2.4 [95% CI = 1.4 to 3.9]; [corrected] all P (trend) < .001). [corrected]. The risk estimates and AUCs for the variation measure were similar to [corrected] those for percent density (AUCs for variation = 0.60-0.62 and [corrected] AUCs for percent density = 0.61-0.65). [corrected]. A meta-analysis of the three studies demonstrated similar associations [corrected] between variation and breast cancer (highest vs lowest quartile: RR = 1.8, 95% CI = 1.4 to 2.3) and [corrected] percent density and breast cancer (highest vs lowest quartile: RR = 2.3, 95% CI = 1.9 to 2.9).
  
  
  CONCLUSIONS
  The association between the automated variation measure and the risk of breast cancer is at least as strong as that for percent density. Efforts to further evaluate and translate the variation measure to the clinical setting are warranted.
authors: Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA.
authors: John J
authors: JJ
authors: Heine
authors: Christopher G
authors: CG
authors: Scott
authors: Thomas A
authors: TA
authors: Sellers
authors: Kathleen R
authors: KR
authors: Brandt
authors: Daniel J
authors: DJ
authors: Serie
authors: Fang-Fang
authors: FF
authors: Wu
authors: Marilyn J
authors: MJ
authors: Morton
authors: Beth A
authors: BA
authors: Schueler
authors: Fergus J
authors: FJ
authors: Couch
authors: Janet E
authors: JE
authors: Olson
authors: V Shane
authors: VS
authors: Pankratz
authors: Celine M
authors: CM
authors: Vachon
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001331:Automation; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology / Q000473:pathology; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000379:methods* / Q000592:standards / Q000639:trends; D008875:Middle Aged; D016016:Proportional Hazards Models; D012372:ROC Curve; D012307:Risk Factors