Literature DB >> 18832239

Single reading with computer-aided detection for screening mammography.

Fiona J Gilbert1, Susan M Astley, Maureen G C Gillan, Olorunsola F Agbaje, Matthew G Wallis, Jonathan James, Caroline R M Boggis, Stephen W Duffy.   

Abstract

BACKGROUND: The sensitivity of screening mammography for the detection of small breast cancers is higher when the mammogram is read by two readers rather than by a single reader. We conducted a trial to determine whether the performance of a single reader using a computer-aided detection system would match the performance achieved by two readers.
METHODS: The trial was designed as an equivalence trial, with matched-pair comparisons between the cancer-detection rates achieved by single reading with computer-aided detection and those achieved by double reading. We randomly assigned 31,057 women undergoing routine screening by film mammography at three centers in England to double reading, single reading with computer-aided detection, or both double reading and single reading with computer-aided detection, at a ratio of 1:1:28. The primary outcome measures were the proportion of cancers detected according to regimen and the recall rates within the group receiving both reading regimens.
RESULTS: The proportion of cancers detected was 199 of 227 (87.7%) for double reading and 198 of 227 (87.2%) for single reading with computer-aided detection (P=0.89). The overall recall rates were 3.4% for double reading and 3.9% for single reading with computer-aided detection; the difference between the rates was small but significant (P<0.001). The estimated sensitivity, specificity, and positive predictive value for single reading with computer-aided detection were 87.2%, 96.9%, and 18.0%, respectively. The corresponding values for double reading were 87.7%, 97.4%, and 21.1%. There were no significant differences between the pathological attributes of tumors detected by single reading with computer-aided detection alone and those of tumors detected by double reading alone.
CONCLUSIONS: Single reading with computer-aided detection could be an alternative to double reading and could improve the rate of detection of cancer from screening mammograms read by a single reader. (ClinicalTrials.gov number, NCT00450359.) 2008 Massachusetts Medical Society

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Year:  2008        PMID: 18832239     DOI: 10.1056/NEJMoa0803545

Source DB:  PubMed          Journal:  N Engl J Med        ISSN: 0028-4793            Impact factor:   91.245


  71 in total

1.  Exploring the potential of context-sensitive CADe in screening mammography.

Authors:  Georgia D Tourassi; Maciej A Mazurowski; Brian P Harrawood; Elizabeth A Krupinski
Journal:  Med Phys       Date:  2010-11       Impact factor: 4.071

Review 2.  Technology as a force for improved diagnosis and treatment of breast disease.

Authors:  Claire M B Holloway; Alexandra Easson; Jaime Escallon; Wey Liang Leong; May Lynn Quan; Michael Reedjik; Frances C Wright; David R McCready
Journal:  Can J Surg       Date:  2010-08       Impact factor: 2.089

3.  Cognition Network Technology prototype of a CAD system for mammography to assist radiologists by finding similar cases in a reference database.

Authors:  Ralf Schönmeyer; Maria Athelogou; Harald Sittek; Peter Ellenberg; Owen Feehan; Günter Schmidt; Gerd Binnig
Journal:  Int J Comput Assist Radiol Surg       Date:  2010-05-26       Impact factor: 2.924

4.  External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

Authors:  Matthias Benndorf; Elizabeth S Burnside; Christoph Herda; Mathias Langer; Elmar Kotter
Journal:  Med Phys       Date:  2015-08       Impact factor: 4.071

5.  Radiological technologists' performance for the detection of malignant microcalcifications in digital mammograms without and with a computer-aided detection system.

Authors:  Rie Tanaka; Miho Takamori; Yoshikazu Uchiyama; Junji Shiraishi
Journal:  J Med Imaging (Bellingham)       Date:  2015-05-27

6.  Single-photon-emission computed tomography (SPECT) with technetium-99m sestamibi in the diagnosis of small breast cancer and axillary lymph node involvement.

Authors:  Alessandro DeCesare; Alessandro de Cesare; Giuseppe De Vincentis; De Vincentis Giuseppe; Stefano Gervasi; Gervasi Stefano; Giacomo Crescentini; Crescentini Giacomo; Enrico Fiori; Fiori Enrico; Marco Bonomi; Bonomi Marco; Alessandro Crocetti; Alessandro Crocetti; Antonio V Sterpetti
Journal:  World J Surg       Date:  2011-12       Impact factor: 3.352

7.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

8.  Short-term outcomes of screening mammography using computer-aided detection: a population-based study of medicare enrollees.

Authors:  Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin
Journal:  Ann Intern Med       Date:  2013-04-16       Impact factor: 25.391

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

Review 10.  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

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