title: Increase in perceived case suspiciousness due to local contrast optimisation in digital screening mammography.
abstract: OBJECTIVE
  To determine the influence of local contrast optimisation on diagnostic accuracy and perceived suspiciousness of digital screening mammograms.
  
  
  METHODS
  Data were collected from a screening region in the Netherlands and consisted of 263 digital screening cases (153 recalled,110 normal). Each case was available twice, once processed with a tissue equalisation (TE) algorithm and once with local contrast optimisation (PV). All cases had digitised previous mammograms. For both algorithms, the probability of malignancy of each finding was scored independently by six screening radiologists. Perceived case suspiciousness was defined as the highest probability of malignancy of all findings of a radiologist within a case. Differences in diagnostic accuracy of the processing algorithms were analysed by comparing the areas under the receiver operating characteristic curves (A(z)). Differences in perceived case suspiciousness were analysed using sign tests.
  
  
  RESULTS
  There was no significant difference in A(z) (TE: 0.909, PV 0.917, P = 0.46). For all radiologists, perceived case suspiciousness using PV was higher than using TE more often than vice versa (ratio: 1.14-2.12). This was significant (P <0.0083) for four radiologists.
  
  
  CONCLUSIONS
  Optimisation of local contrast by image processing may increase perceived case suspiciousness, while diagnostic accuracy may remain similar.
  
  
  CONCLUSIONS
  Variations among different image processing algorithms for digital screening mammography are large. Current algorithms still aim for optimal local contrast with a low dynamic range. Although optimisation of contrast may increase sensitivity, diagnostic accuracy is probably unchanged. Increased local contrast may render both normal and abnormal structures more conspicuous.
authors.affiliation: National Expert and Training Centre for Breast Cancer Screening, P.O. Box 6873, 6503 GJ, Nijmegen, the Netherlands.
authors.forename: Roelant
authors.initials: R
authors.lastname: Visser
authors.forename: Wouter J H
authors.initials: WJ
authors.lastname: Veldkamp
authors.forename: David
authors.initials: D
authors.lastname: Beijerinck
authors.forename: Petra A M
authors.initials: PA
authors.lastname: Bun
authors.forename: Jan J M
authors.initials: JJ
authors.lastname: Deurenberg
authors.forename: Mechli W
authors.initials: MW
authors.lastname: Imhof-Tas
authors.forename: Klaas H
authors.initials: KH
authors.lastname: Schuur
authors.forename: Miranda M
authors.initials: MM
authors.lastname: Snoeren
authors.forename: Gerard J
authors.initials: GJ
authors.lastname: den Heeten
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
authors.forename: Mireille J M
authors.initials: MJ
authors.lastname: Broeders
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology* / Q000517:prevention & control; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D015588:Observer Variation; D015995:Prevalence; D011856:Radiographic Image Enhancement / Q000379:methods*; D018570:Risk Assessment; D012307:Risk Factors
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