title: Mammographic density and risk of breast cancer by age and tumor characteristics.
abstract: BACKGROUND
  Understanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models.
  
  
  METHODS
  Data were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (<55, 55-64, and ≥ 65 years).
  
  
  RESULTS
  MD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma in situ (DCIS) compared to women ages 55-64 and ≥ 65 years (P(age-interaction) = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P's < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55-64 and ≥ 65 years (P(age-interaction) = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group.
  
  
  CONCLUSIONS
  MD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Bertrand
authors.forename: Rulla M
authors.initials: RM
authors.lastname: Tamimi
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Scott
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Jensen
authors.forename: V
authors.initials: V
authors.lastname: Pankratz
authors.forename: Daniel
authors.initials: D
authors.lastname: Visscher
authors.forename: Aaron
authors.initials: A
authors.lastname: Norman
authors.forename: Fergus
authors.initials: F
authors.lastname: Couch
authors.forename: John
authors.initials: J
authors.lastname: Shepherd
authors.forename: Bo
authors.initials: B
authors.lastname: Fan
authors.forename: Yunn-Yi
authors.initials: YY
authors.lastname: Chen
authors.forename: Lin
authors.initials: L
authors.lastname: Ma
authors.forename: Andrew H
authors.initials: AH
authors.lastname: Beck
authors.forename: Steven R
authors.initials: SR
authors.lastname: Cummings
authors.forename: Karla
authors.initials: K
authors.lastname: Kerlikowske
authors.forename: Celine M
authors.initials: CM
authors.lastname: Vachon
mesh_terms: D000367:Age Factors; D000368:Aged; D014408:Biomarkers, Tumor / Q000378:metabolism; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism / Q000473:pathology*; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000000981:diagnostic imaging / Q000378:metabolism / Q000473:pathology; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008198:Lymph Nodes / Q000473:pathology; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008875:Middle Aged; D011859:Radiography; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism; D012307:Risk Factors
chemical_list: D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C508053:ERBB2 protein, human