Literature DB >> 21795668

Effectiveness of computer-aided detection in community mammography practice.

Joshua J Fenton1, Linn Abraham, Stephen H Taplin, Berta M Geller, Patricia A Carney, Carl D'Orsi, Joann G Elmore, William E Barlow.   

Abstract

BACKGROUND: Computer-aided detection (CAD) is applied during screening mammography for millions of US women annually, although it is uncertain whether CAD improves breast cancer detection when used by community radiologists.
METHODS: We investigated the association between CAD use during film-screen screening mammography and specificity, sensitivity, positive predictive value, cancer detection rates, and prognostic characteristics of breast cancers (stage, size, and node involvement). Records from 684 956 women who received more than 1.6 million film-screen mammograms at Breast Cancer Surveillance Consortium facilities in seven states in the United States from 1998 to 2006 were analyzed. We used random-effects logistic regression to estimate associations between CAD and specificity (true-negative examinations among women without breast cancer), sensitivity (true-positive examinations among women with breast cancer diagnosed within 1 year of mammography), and positive predictive value (breast cancer diagnosed after positive mammograms) while adjusting for mammography registry, patient age, time since previous mammography, breast density, use of hormone replacement therapy, and year of examination (1998-2002 vs 2003-2006). All statistical tests were two-sided.
RESULTS: Of 90 total facilities, 25 (27.8%) adopted CAD and used it for an average of 27.5 study months. In adjusted analyses, CAD use was associated with statistically significantly lower specificity (OR = 0.87, 95% confidence interval [CI] = 0.85 to 0.89, P < .001) and positive predictive value (OR = 0.89, 95% CI = 0.80 to 0.99, P = .03). A non-statistically significant increase in overall sensitivity with CAD (OR = 1.06, 95% CI = 0.84 to 1.33, P = .62) was attributed to increased sensitivity for ductal carcinoma in situ (OR = 1.55, 95% CI = 0.83 to 2.91; P = .17), although sensitivity for invasive cancer was similar with or without CAD (OR = 0.96, 95% CI = 0.75 to 1.24; P = .77). CAD was not associated with higher breast cancer detection rates or more favorable stage, size, or lymph node status of invasive breast cancer.
CONCLUSION: CAD use during film-screen screening mammography in the United States is associated with decreased specificity but not with improvement in the detection rate or prognostic characteristics of invasive breast cancer.

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Mesh:

Year:  2011        PMID: 21795668      PMCID: PMC3149041          DOI: 10.1093/jnci/djr206

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  41 in total

1.  How widely is computer-aided detection used in screening and diagnostic mammography?

Authors:  Vijay M Rao; David C Levin; Laurence Parker; Barbara Cavanaugh; Andrea J Frangos; Jonathan H Sunshine
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2.  Computer-aided detection evaluation methods are not created equal.

Authors:  Robert M Nishikawa; Lorenzo L Pesce
Journal:  Radiology       Date:  2009-06       Impact factor: 11.105

Review 3.  The preponderance of evidence supports computer-aided detection for screening mammography.

Authors:  Robyn L Birdwell
Journal:  Radiology       Date:  2009-10       Impact factor: 11.105

Review 4.  Can computer-aided detection be detrimental to mammographic interpretation?

Authors:  Liane E Philpotts
Journal:  Radiology       Date:  2009-10       Impact factor: 11.105

Review 5.  Advanced breast cancer and breast cancer mortality in randomized controlled trials on mammography screening.

Authors:  Philippe Autier; Clarisse Héry; Jari Haukka; Mathieu Boniol; Graham Byrnes
Journal:  J Clin Oncol       Date:  2009-11-02       Impact factor: 44.544

6.  Comparison of two software versions of a commercially available computer-aided detection (CAD) system for detecting breast cancer.

Authors:  Seung Ja Kim; Woo Kyung Moon; Soo-Yeon Kim; Jung Min Chang; Sun Mi Kim; Nariya Cho
Journal:  Acta Radiol       Date:  2010-06       Impact factor: 1.990

Review 7.  Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate.

Authors:  Paul Taylor; Henry W W Potts
Journal:  Eur J Cancer       Date:  2008-03-18       Impact factor: 9.162

Review 8.  Computer-aided detection mammography for breast cancer screening: systematic review and meta-analysis.

Authors:  Meredith Noble; Wendy Bruening; Stacey Uhl; Karen Schoelles
Journal:  Arch Gynecol Obstet       Date:  2008-11-21       Impact factor: 2.344

9.  Does computer-aided detection (CAD) contribute to the performance of digital mammography in a self-referred population?

Authors:  Beniamino Brancato; Nehmat Houssami; Damiana Francesca; Simonetta Bianchi; Gabriella Risso; Sandra Catarzi; Renzo Taschini; Marco Rosselli Del Turco; Stefano Ciatto
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10.  Breast cancer after use of estrogen plus progestin in postmenopausal women.

Authors:  Rowan T Chlebowski; Lewis H Kuller; Ross L Prentice; Marcia L Stefanick; JoAnn E Manson; Margery Gass; Aaron K Aragaki; Judith K Ockene; Dorothy S Lane; Gloria E Sarto; Aleksandar Rajkovic; Robert Schenken; Susan L Hendrix; Peter M Ravdin; Thomas E Rohan; Shagufta Yasmeen; Garnet Anderson
Journal:  N Engl J Med       Date:  2009-02-05       Impact factor: 91.245

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  51 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

2.  Re: effectiveness of computer-aided detection in community mammography practice.

Authors:  Robert M Nishikawa; Maryellen L Giger; Yulei Jiang; Charles E Metz
Journal:  J Natl Cancer Inst       Date:  2011-12-20       Impact factor: 13.506

3.  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

4.  Image toggling saves time in mammography.

Authors:  Trafton Drew; Avi M Aizenman; Matthew B Thompson; Mark D Kovacs; Michael Trambert; Murray A Reicher; Jeremy M Wolfe
Journal:  J Med Imaging (Bellingham)       Date:  2015-10-12

5.  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

6.  Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.

Authors:  Maxine Tan; Jiantao Pu; Bin Zheng
Journal:  Phys Med Biol       Date:  2014-07-17       Impact factor: 3.609

Review 7.  Breast cancer screening: an evidence-based update.

Authors:  Mackenzie S Fuller; Christoph I Lee; Joann G Elmore
Journal:  Med Clin North Am       Date:  2015-03-05       Impact factor: 5.456

8.  Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions.

Authors:  Rohith Reddy Gundreddy; Maxine Tan; Yuchen Qiu; Samuel Cheng; Hong Liu; Bin Zheng
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

9.  A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

Authors:  Yuchen Qiu; Shiju Yan; Rohith Reddy Gundreddy; Yunzhi Wang; Samuel Cheng; Hong Liu; Bin Zheng
Journal:  J Xray Sci Technol       Date:  2017       Impact factor: 1.535

10.  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

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