title: Mammographic density and cancer detection: does digital imaging challenge our current understanding?
abstract: OBJECTIVE
  To investigate the impact of breast density on the performance of radiologists when mammograms are digitally acquired and displayed.
  
  
  METHODS
  A total of 150 craniocaudal digital mammograms including 75 cases with cancer were examined by 14 radiologists divided into two groups: those who read more (six) and less (eight) than 2000 mammograms per year. Cases were classified as low or high mammographic density. For both types of cases, detection of cancers within and outside the dense fibroglandular tissue was investigated. The performance of radiologist was measured using jack-knife free-response receiver operating characteristic (JAFROC) figure of merit (FOM).
  
  
  RESULTS
  Radiologists with over 2000 annual reads had significantly higher JAFROC FOM (P = .03) for high (0.76) mammographic density compared to low (0.70) mammographic density cases. When lesions overlaid the fibroglandular tissue, cases with high mammographic density compared to low mammographic density displayed increased location sensitivity for all radiologists (P = .03) and for those radiologists reading more than 2000 mammograms annually (P = .04), whereas JAFROC FOMs increased for all radiologists (P = .05). No significant changes were observed when the lesion was outside the fibroglandular region.
  
  
  CONCLUSIONS
  Increased mammographic density improves the performance of experienced radiologists when using digital mammograms. This finding, which does not align with those previously reported for film screen systems, may be because of windowing/leveling opportunities available with digital images.
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia. Electronic address: dalm3874@uni.sydney.edu.au.
authors.forename: Dana S
authors.initials: DS
authors.lastname: Al Mousa
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Claudia
authors.initials: C
authors.lastname: Mello-Thoms
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Elaine A
authors.initials: EA
authors.lastname: Ryan
authors.affiliation: BreastScreen, Cancer Institute of NSW, NSW, Australia.
authors.forename: Warwick B
authors.initials: WB
authors.lastname: Lee
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Mariusz W
authors.initials: MW
authors.lastname: Pietrzyk
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Warren M
authors.initials: WM
authors.lastname: Reed
authors.affiliation: Discipline of Behavioural and Social Sciences in Health, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Robert
authors.initials: R
authors.lastname: Heard
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Ann
authors.initials: A
authors.lastname: Poulos
authors.affiliation: Sydney South West Area Health Services (SSWAHS), NSW, Australia.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Tan
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Yanpeng
authors.initials: Y
authors.lastname: Li
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, NSW, Australia.
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Brennan
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods*; D000328:Adult; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D009517:New South Wales; D015588:Observer Variation; D011361:Professional Competence; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Digital mammography; breast cancer; cancer detection; mammographic density; radiologists' performance