Literature DB >> 16040901

Computer-aided detection with screening mammography in a university hospital setting.

Robyn L Birdwell1, Parul Bandodkar, Debra M Ikeda.   

Abstract

PURPOSE: To prospectively assess the effect of computer-aided detection (CAD) on screening mammogram interpretation in an academic medical center to determine if the outcome is different than that previously reported for community practices.
MATERIALS AND METHODS: Institutional review board approval was granted, and informed consent was waived. During a 19-month period, 8682 women (median age, 54 years; range, 33-95 years) underwent screening mammography. Each mammogram was interpreted by one of seven radiologists, followed by immediate re-evaluation of the mammogram with CAD information. Each recalled case was classified as follows: radiologist perceived the finding and CAD marked it, radiologist perceived the finding and CAD did not mark it, or CAD prompted the radiologist to perceive the finding and recall the patient. Lesion type was also recorded. Recalled patients were tracked to determine the effect of CAD on recall and biopsy recommendation rates, positive predictive value (PPV) of biopsy, and cancer detection rate. A 95% confidence interval was calculated for cancer detection rate. Pathologic examination was performed for all cancers.
RESULTS: Of 8682 patients, 863 (9.9%) with 960 findings were recalled for further work-up (Breast Imaging Reporting and Data System category 0). After further diagnostic imaging, it was recommended that biopsy or aspiration be performed for 181 of 960 findings (19%); 165 interventions were confirmed to have been performed. Twenty-nine cancers were found in this group, with a PPV for biopsy of 18% (29 of 165 findings) and a cancer detection rate of 3.3 per 1000 screening mammograms (29 of 8682 patients). CAD-prompted recalls contributed 8% (73 of 960 findings) of total recalled findings and 7% (two of 29 lesions) of cancers detected. Of 29 cancers (59%), 17 manifested as masses and 12 (41%) were microcalcifications. Ten (34%) cancers were ductal carcinoma in situ, and the remaining cancers had an invasive component. Both cancers found with CAD manifested as masses, and both were invasive ductal carcinoma.
CONCLUSION: Prospective clinical use of CAD in a university hospital setting resulted in a 7.4% increase (from 27 to 29) in cancers detected. Both cancers were nonpalpable masses.

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Year:  2005        PMID: 16040901     DOI: 10.1148/radiol.2362040864

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


  39 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.  Dual system approach to computer-aided detection of breast masses on mammograms.

Authors:  Jun Wei; Heang-Ping Chan; Berkman Sahiner; Lubomir M Hadjiiski; Mark A Helvie; Marilyn A Roubidoux; Chuan Zhou; Jun Ge
Journal:  Med Phys       Date:  2006-11       Impact factor: 4.071

Review 4.  Digital mammography: what do we and what don't we know?

Authors:  Ulrich Bick; Felix Diekmann
Journal:  Eur Radiol       Date:  2007-02-14       Impact factor: 5.315

Review 5.  [Workflow in digital screening mammography].

Authors:  U Bick; F Diekmann; E M Fallenberg
Journal:  Radiologe       Date:  2008-04       Impact factor: 0.635

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

Review 7.  [Quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography].

Authors:  E A M Hauth; H Jaeger; S Maderwald; A Mühler; R Kimmig; M Forsting
Journal:  Radiologe       Date:  2008-06       Impact factor: 0.635

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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