title: The perception of breast cancer: what differentiates missed from reported cancers in mammography?
abstract: OBJECTIVE
  Mammographers map endogenous and exogenous factors into decisions whether to report the presence of a malignant finding in a mammogram case. Thus, to understand how image-based elements are translated into observer-based decisions, the authors used spatial frequency analysis to model the areas on mammograms that attracted visual attention, in addition to the areas localized as abnormal.
  
  
  METHODS
  Four mammographers read 40 two-view mammogram cases, of which 30 contained at least one malignant lesion visible on one or two views. Their eye positions were recorded during visual search. Once the mammographer felt confident enough to provide an initial impression of the case ("normal" or "abnormal"), the eye position monitoring was turned off and the mammographer indicated, with a mouse-controlled cursor, the location and nature of any malignant findings. Regions that elicited an overt or a covert response by the mammographers were extracted for processing by means of wavelet packets and artificial neural networks.
  
  
  RESULTS
  Different decision outcomes yielded different energy representations, in the spatial frequency domain. These energy representations were used by an artificial neural network to predict decision outcome in areas of interest, derived from eye position analysis, on mammograms from new cases. Individual trends were observed for each mammographer.
  
  
  CONCLUSIONS
  Spatial frequency representation of regions that attracted a given mammographer's visual attention may be useful for characterizing how that mammographer will respond to the visually selected areas.
authors.affiliation: Department of Radiology, University of Pittsburgh, Magee-Women's Hospital, PA 15213-3180, USA.
authors.forename: Claudia
authors.initials: C
authors.lastname: Mello-Thoms
authors.forename: Stanley
authors.initials: S
authors.lastname: Dunn
authors.forename: Calvin F
authors.initials: CF
authors.lastname: Nodine
authors.forename: Harold L
authors.initials: HL
authors.lastname: Kundel
authors.forename: Susan P
authors.initials: SP
authors.lastname: Weinstein
mesh_terms: D000704:Analysis of Variance; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003657:Decision Making; D003937:Diagnosis, Differential; D003951:Diagnostic Errors; D005260:Female; D006801:Humans; D008327:Mammography; D015588:Observer Variation; D010364:Pattern Recognition, Visual; D012189:Retrospective Studies