Literature DB >> 16244252

Computer-aided detection in the United Kingdom National Breast Screening Programme: prospective study.

Lisanne A L Khoo1, Paul Taylor, Rosalind M Given-Wilson.   

Abstract

PURPOSE: To evaluate prospectively the recall and cancer detection rates with and without computer-aided detection (CAD) in the United Kingdom National Health Service Breast Screening Programme.
MATERIALS AND METHODS: The study had appropriate ethics committee approval. Informed consent was not required; however, patients were informed that their mammograms might be used in research efforts, and all patients agreed to participate. Mammograms obtained in 6111 women (mean age, 58.4 years) undergoing routine screening every 3 years were analyzed with a CAD system. Mammograms were independently double read. Twelve readers participated. Readers recorded an initial evaluation, viewed the CAD prompts, and recorded a final evaluation. Recall to assessment was decided after arbitration. Sensitivities were calculated for single reading, single reading with CAD, and double reading, as a proportion of the total number of cancers detected by using double reading with CAD.
RESULTS: A total of 62 cancers were detected in 61 women. CAD prompted 51 (84%) of 61 radiographically detected cancers. Of 12 cancers missed on single reading, nine were correctly prompted; however, seven prompts were overruled by the reader. Sensitivity of single reading was 90.2% (95% confidence interval [CI]: 83.0%, 95.0%), single reading with CAD was 91.5% (95% CI: 85.0%, 96.0%), and double reading without CAD was 98.4% (95% CI: 91.0%, 100%). Cancer detection rate was 1%. Recall to assessment rate was 6.1%, with an increase of 5.8% because of CAD. Average time required, per reader, to read a case was 25 seconds without CAD and 45 seconds with CAD.
CONCLUSION: CAD increases sensitivity of single reading by 1.3%, whereas double reading increases sensitivity by 8.2%.

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Mesh:

Year:  2005        PMID: 16244252     DOI: 10.1148/radiol.2372041362

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


  31 in total

1.  An interactive system for computer-aided diagnosis of breast masses.

Authors:  Xingwei Wang; Lihua Li; Wei Liu; Weidong Xu; Dror Lederman; Bin Zheng
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

Review 2.  Information technology conduit as a portal to circumvent the graveyard shift.

Authors:  Amar Gupta; Shawna Sando; Sairam Parthasarathy; Stuart F Quan
Journal:  J Clin Sleep Med       Date:  2010-04-15       Impact factor: 4.062

3.  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 4.  [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

5.  Optimization of reference library used in content-based medical image retrieval scheme.

Authors:  Sang Cheol Park; Rahul Sukthankar; Lily Mummert; Mahadev Satyanarayanan; Bin Zheng
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

6.  Should previous mammograms be digitised in the transition to digital mammography?

Authors:  S Taylor-Phillips; M G Wallis; A G Gale
Journal:  Eur Radiol       Date:  2009-03-18       Impact factor: 5.315

Review 7.  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

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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