Effect of computer-aided detection on independent double reading of paired screen-film and full-field digital screening mammograms.
OBJECTIVE
The purpose of this study was to evaluate the performance and potential contribution of computer-aided detection (CAD) to independent double reading of paired screen-film and full-field digital screening mammograms.


METHODS
The cases of 3,683 women who underwent both screen-film mammography and full-field digital mammography (FFDM) with independent double reading for each technique were followed for 2 years to include cancers detected in the interval between screening rounds and cancers detected at the next screening round. Fifty-five biopsy-proven cancers were diagnosed. The baseline screening mammograms of the 55 cancers were defined as having positive findings if at least one of two independent readers scored it 2 or higher on a 5-point rating scale. The baseline mammograms of interval (n = 10) or secondround (n = 16) cancers were retrospectively classified as overlooked (n = 2), minimal sign actionable (n = 8), minimal sign nonactionable (n = 5), and normal (n = 11). The baseline mammograms of these cases of cancer were evaluated with a CAD system, and the CAD results were compared (McNemar's test for paired proportions) with the findings at prospective independent double reading of mammograms obtained with each technique.


RESULTS
For FFDM, CAD sensitivity was 95% (37/39) compared with 64% (25/39) for double reading (p = 0.006), and for screen-film mammography, CAD sensitivity was 85% (33/39) compared with 77% (30/39) for prospective double reading (p = 0.57) of radiographically visible lesions in baseline mammograms. CAD correctly marked five (13%) of 39 cancers on screen-film mammography and 14 (36%) of 39 cancers on FFDM not detected at prospective independent double reading.


CONCLUSIONS
CAD showed the potential to increase the cancer detection rate for FFDM and for screen-film mammography in breast cancer screening performed with independent double reading.
Skaane
Per
P
Department of Radiology, Breast Imaging Center, Ullevaal University Hospital, Kirkeveien 166, N-0407 Oslo, Norway. per.skaane@ulleval.no
Kshirsagar
Ashwini
A
Stapleton
Sandra
S
Young
Kari
K
Castellino
Ronald A
RA
D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods / Q000706:statistics & numerical data*; D008403:Mass Screening / Q000379:methods* / Q000706:statistics & numerical data*; D008875:Middle Aged; D009664:Norway / Q000453:epidemiology; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014962:X-Ray Film; D014963:X-Ray Intensifying Screens