Literature DB >> 16569779

Screening mammograms: interpretation with computer-aided detection--prospective evaluation.

Marilyn J Morton1, Dana H Whaley, Kathleen R Brandt, Kimberly K Amrami.   

Abstract

PURPOSE: To prospectively determine the effect of a commercially available computer-aided detection (CAD) system on interpretations of screening mammograms.
MATERIALS AND METHODS: Institutional review board approval was granted; informed consent and HIPAA compliance were waived. A total of 21 349 screening mammograms obtained in 18 096 women were interpreted first without and then with review of CAD images to determine the effect of CAD analysis on the screening breast cancer detection rate, recall rate, and positive predictive value (PPV) for biopsy. The percentage of total cancers detected by the radiologists independent of CAD and the percentage correctly marked by the CAD system were determined.
RESULTS: On the basis of pre-CAD interpretations, 2101 patients were recalled for diagnostic evaluation, 256 biopsies were performed, and 105 breast cancers were diagnosed. The breast cancer detection rate per 1000 screening mammograms was 4.92 (105 of 21 349 mammograms), the recall rate was 9.84% (2101 of 21 349 mammograms), and the PPV for biopsy was 41.0% (105 of 256 biopsies). After CAD image review, 199 additional patients were recalled, 21 additional biopsies were performed, and eight additional cancers were detected. The effect was a 7.62% (eight of 105) increase in the number of breast cancers detected, an increase in the recall rate to 10.77% (2300 of 21 349 mammograms), and a slight decrease in the PPV to 40.8% (113 of 277 biopsies). Radiologists detected 92.9% (105 of 113 cancers) of the total cancers, and CAD correctly marked 76.1% (86 of 113 cancers).
CONCLUSION: The use of CAD improved the detection of breast cancer, with an acceptable increase in the recall rate and a minimal increase in the number of biopsies with benign results. (c) RSNA, 2006.

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Year:  2006        PMID: 16569779     DOI: 10.1148/radiol.2392042121

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  54 in total

1.  Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach.

Authors:  Berkman Sahiner; Heang-Ping Chan; Lubomir M Hadjiiski; Mark A Helvie; Jun Wei; Chuan Zhou; Yao Lu
Journal:  Med Phys       Date:  2012-01       Impact factor: 4.071

2.  Automated detection of mass lesions in dedicated breast CT: a preliminary study.

Authors:  I Reiser; R M Nishikawa; M L Giger; J M Boone; K K Lindfors; K Yang
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

3.  A comparative study of conventional mammography film interpretations with soft copy readings of the same examinations.

Authors:  Joseph N Gitlin; Anand K Narayan; Chad A Mitchell; Ali M Akmal; David J Eisner; Lindsy M Peterson; Daisy Nie; Tyler R McClintock
Journal:  J Digit Imaging       Date:  2007-03       Impact factor: 4.056

Review 4.  [Current situation and future perspectives of digital mammography].

Authors:  R Schulz-Wendtland; K-P Hermann; T Wacker; W Bautz
Journal:  Radiologe       Date:  2008-04       Impact factor: 0.635

5.  Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers.

Authors:  Tingting Mu; Asoke K Nandi; Rangaraj M Rangayyan
Journal:  J Digit Imaging       Date:  2008-02-28       Impact factor: 4.056

Review 6.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

7.  Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees.

Authors:  Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin
Journal:  Ann Intern Med       Date:  2013-04-16       Impact factor: 25.391

8.  "CADEAT": considerations on the use of CAD (computer-aided diagnosis) in mammography.

Authors:  R Chersevani; S Ciatto; C Del Favero; A Frigerio; L Giordano; G Giuseppetti; C Naldoni; P Panizza; M Petrella; G Saguatti
Journal:  Radiol Med       Date:  2010-01-15       Impact factor: 3.469

Review 9.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

10.  Computer-assisted diagnosis (CAD) in mammography: comparison of diagnostic accuracy of a new algorithm (Cyclopus, Medicad) with two commercial systems.

Authors:  S Ciatto; D Cascio; F Fauci; R Magro; G Raso; R Ienzi; F Martinelli; M Vasile Simone
Journal:  Radiol Med       Date:  2009-05-14       Impact factor: 3.469

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