title: Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment.
abstract: OBJECTIVE
  The purpose of the study was to evaluate the usefulness of power law spectral analysis on mammographic parenchymal patterns in breast cancer risk assessment.
  
  
  METHODS
  Mammograms from 172 subjects (30 women with the BRCA1/BRCA2 gene mutation and 142 low-risk women) were retrospectively collected and digitized. Because age is a very important risk factor, 60 low-risk women were randomly selected from the 142 low-risk subjects and were age matched to the 30 gene mutation carriers. Regions of interest were manually selected from the central breast region behind the nipple of these digitized mammograms and subsequently used in power spectral analysis. The power law spectrum of the form P(f) = B/f(beta) was evaluated for the mammographic patterns. The performance of exponent beta as a decision variable for differentiating between gene mutation carriers and low-risk women was assessed using receiver operating characteristic analysis for both the entire database and the age-matched subset.
  
  
  RESULTS
  Power spectral analysis of mammograms demonstrated a statistically significant difference between the 30 BRCA1/BRCA2 gene mutation carriers and the 142 low risk women with an average beta values of 2.92 (+/-0.28) and 2.47(+/-0.20), respectively. An A (z) value of 0.90 was achieved in distinguishing between gene mutation carriers and low-risk women in the entire database, with an A (z) value of 0.89 being achieved on the age-matched subset.
  
  
  CONCLUSIONS
  The BRCA1/BRCA2 gene mutation carriers and low-risk women have different mammographic parenchymal patterns. It is expected that women identified as high risk by computerized feature analyses might potentially be more aggressively screened for breast cancer.
authors.affiliation: Department of Radiology, The University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA. huili@uchicago.edu
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
authors.forename: Olufunmilayo I
authors.initials: OI
authors.lastname: Olopade
authors.forename: Michael R
authors.initials: MR
authors.lastname: Chinander
mesh_terms: D000367:Age Factors; D000465:Algorithms; D019313:BRCA1 Protein / Q000235:genetics; D024682:BRCA2 Protein / Q000235:genetics; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000235:genetics; D005260:Female; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D008327:Mammography; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*
chemical_list: D019313:BRCA1 Protein; D024682:BRCA2 Protein