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AJR Am J Roentgenol ; 188(2): 377-84, 2007 Feb.
Article in English | MEDLINE | ID: mdl-17242245

ABSTRACT

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. MATERIALS AND 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. CONCLUSION: 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.


Subject(s)
Breast Neoplasms/diagnostic imaging , Breast Neoplasms/epidemiology , Mammography/statistics & numerical data , Mass Screening/methods , Mass Screening/statistics & numerical data , Pattern Recognition, Automated/methods , Radiographic Image Interpretation, Computer-Assisted/methods , Artificial Intelligence , Female , Humans , Mammography/methods , Middle Aged , Norway/epidemiology , Observer Variation , Radiographic Image Enhancement , Reproducibility of Results , Sensitivity and Specificity , X-Ray Film , X-Ray Intensifying Screens
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