Literature DB >> 20656831

Mammographic features of breast cancers at single reading with computer-aided detection and at double reading in a large multicenter prospective trial of computer-aided detection: CADET II.

Jonathan J James1, Fiona J Gilbert, Matthew G Wallis, Maureen G C Gillan, Susan M Astley, Caroline R M Boggis, Olorunsola F Agbaje, Adam R Brentnall, Stephen W Duffy.   

Abstract

PURPOSE: To evaluate the mammographic features of breast cancer that favor lesion detection with single reading and computer-aided detection (CAD) or with double reading.
MATERIALS AND METHODS: The Computer Aided Detection Evaluation Trial II study was approved by the ethics committee, and all participants provided written informed consent. A total of 31,057 women were recruited from three screening centers between September 2006 and August 2007. They were randomly allocated to the double reading group, the single reading with CAD group, or the double reading and single reading with CAD group at a ratio of 1:1:28, respectively. In this study, cancers in the women whose mammograms were read with both single reading with CAD and double reading were retrospectively reviewed. The original mammograms were obtained for each case and reviewed by two of three experienced breast radiologists in consensus. The method of detection was noted. The size and predominant mammographic feature of the cancer were recorded, as was the breast density. CAD marking data were reviewed to determine if the cancer had been correctly marked.
RESULTS: A total of 227 cancers were detected in 28,204 women. A total of 170 cases were recalled with both reading regimens. Lesion types were masses (66%), microcalcifications (25%), parenchymal deformities (6%), and asymmetric densities (3%). The ability of the reading regimens to correctly prompt the reader to recall cases varied significantly by lesion type (P < .001). More parenchymal deformities were recalled with double reading, whereas more asymmetric densities were recalled with single reading with CAD. There was no difference in the ability of either reading regimen to prompt the reader to correctly recall masses or microcalcifications. CAD correctly prompted 100% of microcalcifications, 87% of mass lesions, 80% of asymmetric densities, and 50% of parenchymal deformities. CAD correctly marked 93% of spiculated masses compared with 80% of ill-defined masses (P = .054). There was a significant trend for cancers detected with double reading to occur only in women with a denser mammographic background pattern (P = .02). Size had no effect on lesion detection.
CONCLUSION: Readers using either single reading with CAD or double reading need to be aware of the strengths and weaknesses of reading regimens to avoid missing the more challenging cancer cases.

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Year:  2010        PMID: 20656831     DOI: 10.1148/radiol.10091899

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


  10 in total

1.  The role of informatics in health care reform.

Authors:  Yueyi I Liu; Daniel L Rubin
Journal:  Acad Radiol       Date:  2012-07-06       Impact factor: 3.173

2.  Prediction of reader estimates of mammographic density using convolutional neural networks.

Authors:  Georgia V Ionescu; Martin Fergie; Michael Berks; Elaine F Harkness; Johan Hulleman; Adam R Brentnall; Jack Cuzick; D Gareth Evans; Susan M Astley
Journal:  J Med Imaging (Bellingham)       Date:  2019-01-31

3.  Breast cancer risk prediction model: a nomogram based on common mammographic screening findings.

Authors:  J M H Timmers; A L M Verbeek; J IntHout; R M Pijnappel; M J M Broeders; G J den Heeten
Journal:  Eur Radiol       Date:  2013-04-18       Impact factor: 5.315

4.  Impact of computer-aided detection systems on radiologist accuracy with digital mammography.

Authors:  Elodia B Cole; Zheng Zhang; Helga S Marques; R Edward Hendrick; Martin J Yaffe; Etta D Pisano
Journal:  AJR Am J Roentgenol       Date:  2014-10       Impact factor: 3.959

5.  Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.

Authors:  Emily F Conant; Alicia Y Toledano; Senthil Periaswamy; Sergei V Fotin; Jonathan Go; Justin E Boatsman; Jeffrey W Hoffmeister
Journal:  Radiol Artif Intell       Date:  2019-07-31

6.  Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool.

Authors:  Serena Pacilè; January Lopez; Pauline Chone; Thomas Bertinotti; Jean Marie Grouin; Pierre Fillard
Journal:  Radiol Artif Intell       Date:  2020-11-04

Review 7.  Is single reading with computer-aided detection (CAD) as good as double reading in mammography screening? A systematic review.

Authors:  Edward Azavedo; Sophia Zackrisson; Ingegerd Mejàre; Marianne Heibert Arnlind
Journal:  BMC Med Imaging       Date:  2012-07-24       Impact factor: 1.930

Review 8.  Challenges and opportunities for artificial intelligence in oncological imaging.

Authors:  H M C Cheung; D Rubin
Journal:  Clin Radiol       Date:  2021-04-24       Impact factor: 3.389

9.  The optimal use of computer aided detection to find low prevalence cancers.

Authors:  Melina A Kunar
Journal:  Cogn Res Princ Implic       Date:  2022-02-04

Review 10.  Errors in Mammography Cannot be Solved Through Technology Alone

Authors:  Ernest Usang Ekpo; Maram Alakhras; Patrick Brennan
Journal:  Asian Pac J Cancer Prev       Date:  2018-02-26
  10 in total

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