title: Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem-solving mammography: effects of image processing and lesion type.
abstract: OBJECTIVE
  To determine effects of lesion type (calcification vs mass) and image processing on radiologist's performance for area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for detection of masses and calcifications with digital mammography in women with mammographically dense breasts.
  
  
  METHODS
  This study included 201 women who underwent digital mammography at seven U.S. and Canadian medical centers. Three image-processing algorithms were applied to the digital images, which were acquired with Fischer, General Electric, and Lorad digital mammography units. Eighteen readers participated in the reader study (six readers per algorithm). Baseline values for reader performance with screen-film mammograms were obtained through the additional interpretation of 179 screen-film mammograms. A repeated-measures analysis of covariance allowing unequal slopes was used in each of the nine analyses (AUC, sensitivity, and specificity for each of three machines). Bonferroni correction was used.
  
  
  RESULTS
  Although lesion type did not affect the AUC or sensitivity for Fischer digital images, it did affect specificity (P =.0004). For the General Electric digital images, AUC, sensitivity, and specificity were not affected by lesion type. For Lorad digital images, the results strongly suggested that lesion type affected AUC and sensitivity (P <.0001). None of the three image-processing methods tested affected the AUC, sensitivity, or specificity for the Fischer, General Electric, or Lorad digital images.
  
  
  CONCLUSIONS
  Findings in this study indicate that radiologist's interpretation accuracy in interpreting digital mammograms depends on lesion type. Interpretation accuracy was not influenced by the image-processing method.
authors.affiliation: Department of Radiology, Lineberger Comprehensive Cancer Center, University of North Carolina School of Medicine, Chapel Hill, NC 27599-7510, USA.
authors.forename: Elodia B
authors.initials: EB
authors.lastname: Cole
authors.forename: Etta D
authors.initials: ED
authors.lastname: Pisano
authors.forename: Emily O
authors.initials: EO
authors.lastname: Kistner
authors.forename: Keith E
authors.initials: KE
authors.lastname: Muller
authors.forename: Marylee E
authors.initials: ME
authors.lastname: Brown
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Feig
authors.forename: Roberta A
authors.initials: RA
authors.lastname: Jong
authors.forename: Andrew D A
authors.initials: AD
authors.lastname: Maidment
authors.forename: Melinda J
authors.initials: MJ
authors.lastname: Staiger
authors.forename: Cherie M
authors.initials: CM
authors.lastname: Kuzmiak
authors.forename: Rita I
authors.initials: RI
authors.lastname: Freimanis
authors.forename: Nadine
authors.initials: N
authors.lastname: Lesko
authors.forename: Eric L
authors.initials: EL
authors.lastname: Rosen
authors.forename: Ruth
authors.initials: R
authors.lastname: Walsh
authors.forename: Margaret
authors.initials: M
authors.lastname: Williford
authors.forename: M Patricia
authors.initials: MP
authors.lastname: Braeuning
mesh_terms: D019540:Area Under Curve; D001940:Breast / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography; D011856:Radiographic Image Enhancement; D012680:Sensitivity and Specificity