Literature DB >> 12616008

Computer-aided detection versus independent double reading of masses on mammograms.

Nico Karssemeijer1, Johannes D M Otten, Andre L M Verbeek, Johanna H Groenewoud, Harry J de Koning, Jan H C L Hendriks, Roland Holland.   

Abstract

PURPOSE: To evaluate the use of a computer-aided detection (CAD) system (designed for mammographic mass detection) to help improve mass interpretation and to compare CAD results with independent double-reading results.
MATERIALS AND METHODS: Screening mammograms from 500 cases were collected; 125 of these cases were screening-detected cancers, and 125 were interval cancers. Previously obtained screening mammograms (ie, prior mammograms) were available in all cases. All mammograms were analyzed by a CAD system, which detected mass regions and assigned a level of (cancer) suspicion to each mass. Ten experienced screening radiologists read the prior mammograms. For independent interpretation with CAD, the suspicion rating assigned to each finding by the radiologist was weighted with the CAD output at the area of the finding. CAD markers on areas that were not reported by the radiologist were not used. Independent double reading was implemented by using a rule to combine the levels of suspicion assigned to findings by two radiologists. Results were evaluated by using localized-response receiver operating characteristic analysis.
RESULTS: In a total of 141 cases, there was a visible abnormality at the location of the cancer on the prior mammogram, and 115 of these were classified as mass cases. For prior mammograms that depicted masses, the mean sensitivity of the radiologists, as averaged among the false-positive rates lower than 10%, was 39.4%; this increased by 7.0% with CAD and by 10.5% with double reading. Differences among single, double, and CAD readings were statistically significant (P <.001).
CONCLUSION: Although independent double reading yields the best detection performance, the presence and probability of CAD mass markers can improve mammogram interpretation.

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Year:  2003        PMID: 12616008     DOI: 10.1148/radiol.2271011962

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


  22 in total

1.  Consensus versus disagreement in imaging research: a case study using the LIDC database.

Authors:  Dmitriy Zinovev; Yujie Duo; Daniela S Raicu; Jacob Furst; Samuel G Armato
Journal:  J Digit Imaging       Date:  2012-06       Impact factor: 4.056

2.  Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses.

Authors:  Rianne Hupse; Maurice Samulski; Marc Lobbes; Ard den Heeten; Mechli W Imhof-Tas; David Beijerinck; Ruud Pijnappel; Carla Boetes; Nico Karssemeijer
Journal:  Eur Radiol       Date:  2012-07-08       Impact factor: 5.315

Review 3.  [Clinical results of digital mammography].

Authors:  R Schulz-Wendtland; K-P Hermann; W Bautz
Journal:  Radiologe       Date:  2005-03       Impact factor: 0.635

4.  Mammographic mass detection using a mass template.

Authors:  Serhat Ozekes; Onur Osman; A Yilmaz Camurcu
Journal:  Korean J Radiol       Date:  2005 Oct-Dec       Impact factor: 3.500

Review 5.  [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

Review 6.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

7.  Integrating CAD modules in a PACS environment using a wide computing infrastructure.

Authors:  Jorge J Suárez-Cuenca; Amara Tilve; Ricardo López; Gonzalo Ferro; Javier Quiles; Miguel Souto
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-02-10       Impact factor: 2.924

8.  Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

Authors:  Corbin A Cunningham; Trafton Drew; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2017-02       Impact factor: 2.199

9.  "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

10.  Using computer-aided detection in mammography as a decision support.

Authors:  Maurice Samulski; Rianne Hupse; Carla Boetes; Roel D M Mus; Gerard J den Heeten; Nico Karssemeijer
Journal:  Eur Radiol       Date:  2010-06-09       Impact factor: 5.315

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