title: Characterizing mammographic images by using generic texture features.
abstract: BACKGROUND
  Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design.
  
  
  METHODS
  A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model.
  
  
  RESULTS
  Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model.
  
  
  CONCLUSIONS
  Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy.
authors.affiliation: University Breast Center for Franconia, Erlangen-Nuremberg Comprehensive Cancer Center, Erlangen University Hospital, Department of Gynecology and Obstetrics, Universitaetsstrasse 21-23, 91054 Erlangen, Germany.
authors.forename: Lothar
authors.initials: L
authors.lastname: Häberle
authors.forename: Florian
authors.initials: F
authors.lastname: Wagner
authors.forename: Peter A
authors.initials: PA
authors.lastname: Fasching
authors.forename: Sebastian M
authors.initials: SM
authors.lastname: Jud
authors.forename: Katharina
authors.initials: K
authors.lastname: Heusinger
authors.forename: Christian R
authors.initials: CR
authors.lastname: Loehberg
authors.forename: Alexander
authors.initials: A
authors.lastname: Hein
authors.forename: Christian M
authors.initials: CM
authors.lastname: Bayer
authors.forename: Carolin C
authors.initials: CC
authors.lastname: Hack
authors.forename: Michael P
authors.initials: MP
authors.lastname: Lux
authors.forename: Katja
authors.initials: K
authors.lastname: Binder
authors.forename: Matthias
authors.initials: M
authors.lastname: Elter
authors.forename: Christian
authors.initials: C
authors.lastname: Münzenmayer
authors.forename: Rüdiger
authors.initials: R
authors.lastname: Schulz-Wendtland
authors.forename: Martina
authors.initials: M
authors.lastname: Meier-Meitinger
authors.forename: Boris R
authors.initials: BR
authors.lastname: Adamietz
authors.forename: Michael
authors.initials: M
authors.lastname: Uder
authors.forename: Matthias W
authors.initials: MW
authors.lastname: Beckmann
authors.forename: Thomas
authors.initials: T
authors.lastname: Wittenberg
mesh_terms: D000368:Aged; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D016017:Odds Ratio; D012307:Risk Factors