Literature DB >> 8153302

Computer-aided mammographic screening for spiculated lesions.

W P Kegelmeyer1, J M Pruneda, P D Bourland, A Hillis, M W Riggs, M L Nipper.   

Abstract

PURPOSE: To study the use of a computer vision method as a second reader for the detection of spiculated lesions on screening mammograms.
MATERIALS AND METHODS: An algorithmic computer process for the detection of spiculated lesions on digitized screen-film mammograms was applied to 85 four-view clinical cases: 36 cases with cancer proved by means of biopsy and 49 cases with negative findings at examination and follow-up. The computer detections were printed as film with added outlines that indicated the suspected cancers. Four radiologists screened the 85 cases twice, once without and once with the computer reports as ancillary films.
RESULTS: The algorithm alone achieved 100% sensitivity, with a specificity of 82%. The computer reports increased the average radiologist sensitivity by 9.7% (P = .005), moving from 80.6% to 90.3%, with no decrease in average specificity.
CONCLUSION: The study demonstrated that computer analysis of mammograms can provide a substantial and statistically significant increase in radiologist screening efficacy.

Entities:  

Mesh:

Year:  1994        PMID: 8153302     DOI: 10.1148/radiology.191.2.8153302

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


  22 in total

1.  Breast masses detection using phase portrait analysis and fuzzy inference systems.

Authors:  Arianna Mencattini; Marcello Salmeri
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-10-11       Impact factor: 2.924

Review 2.  [Clinical results of digital mammography].

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

3.  Computer-aided detection of breast masses on full field digital mammograms.

Authors:  Jun Wei; Berkman Sahiner; Lubomir M Hadjiiski; Heang-Ping Chan; Nicholas Petrick; Mark A Helvie; Marilyn A Roubidoux; Jun Ge; Chuan Zhou
Journal:  Med Phys       Date:  2005-09       Impact factor: 4.071

4.  Usefulness of texture analysis for computerized classification of breast lesions on mammograms.

Authors:  Roberto R Pereira; Paulo M Azevedo Marques; Marcelo O Honda; Sergio K Kinoshita; Roger Engelmann; Chisako Muramatsu; Kunio Doi
Journal:  J Digit Imaging       Date:  2007-09       Impact factor: 4.056

5.  Classification of breast masses in mammograms using genetic programming and feature selection.

Authors:  R J Nandi; A K Nandi; R M Rangayyan; D Scutt
Journal:  Med Biol Eng Comput       Date:  2006-07-21       Impact factor: 2.602

6.  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

7.  Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library.

Authors:  Bin Zheng; Claudia Mello-Thoms; Xiao-Hui Wang; Gordon S Abrams; Jules H Sumkin; Denise M Chough; Marie A Ganott; Amy Lu; David Gur
Journal:  Acad Radiol       Date:  2007-08       Impact factor: 3.173

Review 8.  [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 9.  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

10.  A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows.

Authors:  Mitsutaka Nemoto; Soshi Honmura; Akinobu Shimizu; Daisuke Furukawa; Hidefumi Kobatake; Shigeru Nawano
Journal:  Int J Comput Assist Radiol Surg       Date:  2008-10-28       Impact factor: 2.924

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