title: Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status.
abstract: Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.
  
  
  
  This study combines five case-control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study.
  
  
  
  Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH_10 [corrected] and FD_TH_15) [corrected] were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH_60 to FD_TH_85) [corrected] were associated with a decreased risk. Increasing the FD_TH_75 [corrected] and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with an increased [corrected] risk of breast cancer. For example, 1 standard deviation increase of FD_TH_75 [corrected] was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79-0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar.
  
  
  
  Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.
authors.affiliation: Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA. serghei.malkov@ucsf.edu.
authors.forename: Serghei
authors.identifier: 0000-0003-0548-6022
authors.initials: S
authors.lastname: Malkov
authors.affiliation: Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA.
authors.forename: John A
authors.initials: JA
authors.lastname: Shepherd
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Scott
authors.affiliation: Harvard Medical School, Boston, MA, USA.
authors.forename: Rulla M
authors.initials: RM
authors.lastname: Tamimi
authors.affiliation: UCSF Departments of Medicine and Epidemiology/Biostatistics, San Francisco, CA, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Ma
authors.affiliation: Slone Epidemiology Center at Boston University, Boston, MA, USA.
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Bertrand
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Fergus
authors.initials: F
authors.lastname: Couch
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Jensen
authors.affiliation: Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA.
authors.forename: Amir P
authors.initials: AP
authors.lastname: Mahmoudzadeh
authors.affiliation: Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Fan
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Norman
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Kathleen R
authors.initials: KR
authors.lastname: Brandt
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: V Shane
authors.initials: VS
authors.lastname: Pankratz
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Celine M
authors.initials: CM
authors.lastname: Vachon
authors.affiliation: UCSF Departments of Medicine and Epidemiology/Biostatistics, San Francisco, CA, USA.
authors.forename: Karla
authors.initials: K
authors.lastname: Kerlikowske
mesh_terms: D000368:Aged; D015992:Body Mass Index; D000071060:Breast Density; D001943:Breast Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000378:metabolism; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000378:metabolism; D016022:Case-Control Studies; D005260:Female; D017709:Fractals; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000379:methods*; D008403:Mass Screening / Q000379:methods; D008875:Middle Aged; D011960:Receptors, Estrogen / Q000378:metabolism*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors
chemical_list: D011960:Receptors, Estrogen