title: Classification of normal screening mammograms is strongly influenced by perceived mammographic breast density.
abstract: BACKGROUND
  To investigate how breast screen readers classify normal screening cases using descriptors of normal mammographic features and to assess test cases for suitability for a single reading strategy.
  
  
  METHODS
  Fifteen breast screen readers interpreted a test set of 29 normal screening cases and classified them by firstly rating their perceived difficulty to reach a 'normal' decision, secondly identifying the cases' salient normal mammographic features and thirdly assessing the cases' suitability for a single reading strategy.
  
  
  RESULTS
  The relationship between the perceived difficulty in making 'normal' decisions and the normal mammographic features was investigated. Regular ductal pattern (Tb= -0.439, P = 0.001), uniform density (Tb= -0.527, P < 0.001), non-dense breasts (Tb= -0.736, P < 0.001), symmetrical mammographic features (Tb= -0.474, P = 0.001) and overlapped density (Tb= 0.630, P < 0.001) had a moderate to strong correlation with the difficulty to make 'normal' decisions. Cases with regular ductal pattern (Tb= 0.447, P = 0.002), uniform density (Tb= 0.550, P < 0.001), non-dense breasts (Tb= 0.748, P < 0.001) and symmetrical mammographic features (Tb= 0.460, P = 0.001) were considered to be more suitable for single reading, whereas cases with overlapped density were not (Tb= -0.679, P < 0.001).
  
  
  CONCLUSIONS
  The findings suggest that perceived mammographic breast density has a major influence on the difficulty for readers to classify cases as normal and hence their suitability for single reading.
authors.affiliation: Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.|National Healthcare Group Diagnostics (NHGD), Singapore City, Singapore.
authors.forename: Zoey Zy
authors.initials: ZZ
authors.lastname: Ang
authors.affiliation: Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.|Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan.
authors.forename: Mohammad A
authors.initials: MA
authors.lastname: Rawashdeh
authors.affiliation: Health Systems and Global Populations Research Group, Faculty of Health Sciences, Discipline of Behavioural and Social Sciences in Health, The University of Sydney, Lidcombe, New South Wales, Australia.
authors.forename: Rob
authors.initials: R
authors.lastname: Heard
authors.affiliation: Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Brennan
authors.affiliation: Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.
authors.forename: Warwick
authors.initials: W
authors.lastname: Lee
authors.affiliation: Medical Imaging Optimisation and Perception Group (MIOPeG), Faculty of Health Sciences, Discipline of Medical Radiation Sciences, The University of Sydney, Lidcombe, New South Wales, Australia.
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Lewis
mesh_terms: D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002983:Clinical Competence; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008327:Mammography; D011793:Queensland; D012307:Risk Factors
keywords: breast density; normal mammograms; reader strategy; screening mammography