title: The mammographic density of a mass is a significant predictor of breast cancer.
abstract: OBJECTIVE
  To determine whether the mammographic density of noncalcified solid breast masses is associated with malignancy and to measure the agreement between prospective and retrospective assessment.
  
  
  METHODS
  The institutional review board approved this study and waived informed consent. Three hundred forty-eight consecutive breast masses in 328 women who underwent image-guided or surgical biopsy between October 2005 and December 2007 were included. All 348 biopsy-proved masses were randomized and assigned to a radiologist who was blinded to biopsy results for retrospective assessment by using the Breast Imaging Reporting and Data System (retrospectively assessed data set). Clinical radiologists prospectively assessed the density of 180 of these masses (prospectively assessed data set). Pathologic result at biopsy was the reference standard. Benign masses were followed for at least 1 year by linking each patient to a cancer registry. Univariate analyses were performed on the retrospectively assessed data set. The association of mass density and malignancy was examined by creating a logistic model for the prospectively assessed data set. Agreement between prospective and retrospective assessments was calculated by using the κ statistic.
  
  
  RESULTS
  In the retrospectively assessed data set, 70.2% of high-density masses were malignant, and 22.3% of the isodense or low-density masses were malignant (P < .0001). In the prospective logistic model, high density (odds ratio, 6.6), irregular shape (odds ratio, 9.9), spiculated margin (odds ratio, 20.3), and age (β = 0.09, P < .0001) were significantly associated with the probability of malignancy. The κ value for prospective-retrospective agreement of mass density was 0.53.
  
  
  CONCLUSIONS
  High mass density is significantly associated with malignancy in both retrospectively and prospectively assessed data sets, with moderate prospective-retrospective agreement. Radiologists should consider mass density as a valuable descriptor that can stratify risk.
  
  
  BACKGROUND
  http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.10100328/-/DC1.
authors.affiliation: Department of Radiology, School of Medicine and Public Health, University of Wisconsin, 600 Highland Ave, E3/366 Clinical Science Center, Madison, WI 53792-3252, USA.
authors.forename: Ryan W
authors.initials: RW
authors.lastname: Woods
authors.forename: Gale S
authors.initials: GS
authors.lastname: Sisney
authors.forename: Lonie R
authors.initials: LR
authors.lastname: Salkowski
authors.forename: Kazuhiko
authors.initials: K
authors.lastname: Shinki
authors.forename: Yunzhi
authors.initials: Y
authors.lastname: Lin
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
mesh_terms: D001706:Biopsy; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D016009:Chi-Square Distribution; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000379:methods*; D011446:Prospective Studies; D012189:Retrospective Studies