Literature DB >> 27918707

National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium.

Constance D Lehman1, Robert F Arao1, Brian L Sprague1, Janie M Lee1, Diana S M Buist1, Karla Kerlikowske1, Louise M Henderson1, Tracy Onega1, Anna N A Tosteson1, Garth H Rauscher1, Diana L Miglioretti1.   

Abstract

Purpose To establish performance benchmarks for modern screening digital mammography and assess performance trends over time in U.S. community practice. Materials and Methods This HIPAA-compliant, institutional review board-approved study measured the performance of digital screening mammography interpreted by 359 radiologists across 95 facilities in six Breast Cancer Surveillance Consortium (BCSC) registries. The study included 1 682 504 digital screening mammograms performed between 2007 and 2013 in 792 808 women. Performance measures were calculated according to the American College of Radiology Breast Imaging Reporting and Data System, 5th edition, and were compared with published benchmarks by the BCSC, the National Mammography Database, and performance recommendations by expert opinion. Benchmarks were derived from the distribution of performance metrics across radiologists and were presented as 50th (median), 10th, 25th, 75th, and 90th percentiles, with graphic presentations using smoothed curves. Results Mean screening performance measures were as follows: abnormal interpretation rate (AIR), 11.6 (95% confidence interval [CI]: 11.5, 11.6); cancers detected per 1000 screens, or cancer detection rate (CDR), 5.1 (95% CI: 5.0, 5.2); sensitivity, 86.9% (95% CI: 86.3%, 87.6%); specificity, 88.9% (95% CI: 88.8%, 88.9%); false-negative rate per 1000 screens, 0.8 (95% CI: 0.7, 0.8); positive predictive value (PPV) 1, 4.4% (95% CI: 4.3%, 4.5%); PPV2, 25.6% (95% CI: 25.1%, 26.1%); PPV3, 28.6% (95% CI: 28.0%, 29.3%); cancers stage 0 or 1, 76.9%; minimal cancers, 57.7%; and node-negative invasive cancers, 79.4%. Recommended CDRs were achieved by 92.1% of radiologists in community practice, and 97.1% achieved recommended ranges for sensitivity. Only 59.0% of radiologists achieved recommended AIRs, and only 63.0% achieved recommended levels of specificity. Conclusion The majority of radiologists in the BCSC surpass cancer detection recommendations for screening mammography; however, AIRs continue to be higher than the recommended rate for almost half of radiologists interpreting screening mammograms. © RSNA, 2016 Online supplemental material is available for this article.

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Year:  2016        PMID: 27918707      PMCID: PMC5375631          DOI: 10.1148/radiol.2016161174

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


  24 in total

1.  MAMMOGRAPHY AS A SCREENING EXAMINATION IN BREAST CANCER.

Authors:  J N WOLFE
Journal:  Radiology       Date:  1965-04       Impact factor: 11.105

2.  Comparative effectiveness of digital versus film-screen mammography in community practice in the United States: a cohort study.

Authors:  Karla Kerlikowske; Rebecca A Hubbard; Diana L Miglioretti; Berta M Geller; Bonnie C Yankaskas; Constance D Lehman; Stephen H Taplin; Edward A Sickles
Journal:  Ann Intern Med       Date:  2011-10-18       Impact factor: 25.391

3.  Digital breast tomosynthesis: observer performance study.

Authors:  David Gur; Gordon S Abrams; Denise M Chough; Marie A Ganott; Christiane M Hakim; Ronald L Perrin; Grace Y Rathfon; Jules H Sumkin; Margarita L Zuley; Andriy I Bandos
Journal:  AJR Am J Roentgenol       Date:  2009-08       Impact factor: 3.959

4.  Diagnostic performance of digital versus film mammography for breast-cancer screening.

Authors:  Etta D Pisano; Constantine Gatsonis; Edward Hendrick; Martin Yaffe; Janet K Baum; Suddhasatta Acharyya; Emily F Conant; Laurie L Fajardo; Lawrence Bassett; Carl D'Orsi; Roberta Jong; Murray Rebner
Journal:  N Engl J Med       Date:  2005-09-16       Impact factor: 91.245

5.  The Gothenburg breast screening trial: first results on mortality, incidence, and mode of detection for women ages 39-49 years at randomization.

Authors:  N Bjurstam; L Björneld; S W Duffy; T C Smith; E Cahlin; O Eriksson; L O Hafström; H Lingaas; J Mattsson; S Persson; C M Rudenstam; J Säve-Söderbergh
Journal:  Cancer       Date:  1997-12-01       Impact factor: 6.860

6.  Identifying minimally acceptable interpretive performance criteria for screening mammography.

Authors:  Patricia A Carney; Edward A Sickles; Barbara S Monsees; Lawrence W Bassett; R James Brenner; Stephen A Feig; Robert A Smith; Robert D Rosenberg; T Andrew Bogart; Sally Browning; Jane W Barry; Mary M Kelly; Khai A Tran; Diana L Miglioretti
Journal:  Radiology       Date:  2010-05       Impact factor: 11.105

7.  Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare.

Authors:  L Tabár; C J Fagerberg; A Gad; L Baldetorp; L H Holmberg; O Gröntoft; U Ljungquist; B Lundström; J C Månson; G Eklund
Journal:  Lancet       Date:  1985-04-13       Impact factor: 79.321

8.  Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.

Authors:  Jeffrey A Tice; Steven R Cummings; Rebecca Smith-Bindman; Laura Ichikawa; William E Barlow; Karla Kerlikowske
Journal:  Ann Intern Med       Date:  2008-03-04       Impact factor: 25.391

9.  Mammographic breast cancer screening--a randomized trial in Malmö, Sweden.

Authors:  I Andersson; L Janzon; B F Sigfússon
Journal:  Maturitas       Date:  1985-05       Impact factor: 4.342

Review 10.  The benefits and harms of breast cancer screening: an independent review.

Authors: 
Journal:  Lancet       Date:  2012-10-30       Impact factor: 79.321

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  100 in total

Review 1.  Newer Technologies in Breast Cancer Imaging: Dedicated Cone-Beam Breast Computed Tomography.

Authors:  Avice M O'Connell; Andrew Karellas; Srinivasan Vedantham; Daniel T Kawakyu-O'Connor
Journal:  Semin Ultrasound CT MR       Date:  2017-09-05       Impact factor: 1.875

2.  Digital Breast Tomosynthesis: Radiologist Learning Curve.

Authors:  Diana L Miglioretti; Linn Abraham; Christoph I Lee; Diana S M Buist; Sally D Herschorn; Brian L Sprague; Louise M Henderson; Anna N A Tosteson; Karla Kerlikowske
Journal:  Radiology       Date:  2019-02-26       Impact factor: 11.105

3.  Development and Validation of eRADAR: A Tool Using EHR Data to Detect Unrecognized Dementia.

Authors:  Deborah E Barnes; Jing Zhou; Rod L Walker; Eric B Larson; Sei J Lee; W John Boscardin; Zachary A Marcum; Sascha Dublin
Journal:  J Am Geriatr Soc       Date:  2019-10-14       Impact factor: 5.562

4.  Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium.

Authors:  B L Sprague; K Kerlikowske; E J A Bowles; G H Rauscher; C I Lee; A N A Tosteson; D L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2019-06-01       Impact factor: 13.506

5.  Impact of New Technology Adoption on Breast Cancer Screening.

Authors:  Christoph I Lee; Janie M Lee
Journal:  Radiology       Date:  2018-12-11       Impact factor: 11.105

Review 6.  Bayes' formula: a powerful but counterintuitive tool for medical decision-making.

Authors:  M P K Webb; D Sidebotham
Journal:  BJA Educ       Date:  2020-04-19

7.  Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.

Authors:  Kadie Clancy; Sarah Aboutalib; Aly Mohamed; Jules Sumkin; Shandong Wu
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

8.  Test Sets and Real-Life Performance: Can One Predict the Other?

Authors:  Denise Thigpen; Jocelyn Rapelyea
Journal:  Radiol Imaging Cancer       Date:  2020-09-25

9.  Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

Authors:  Hari M Trivedi; Maryam Panahiazar; April Liang; Dmytro Lituiev; Peter Chang; Jae Ho Sohn; Yunn-Yi Chen; Benjamin L Franc; Bonnie Joe; Dexter Hadley
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

10.  Mammography Performance Benchmarks in an Era of Value-based Care.

Authors:  Janie M Lee; Diana L Miglioretti; Elizabeth S Burnside; Elizabeth A Morris; Robert A Smith; Constance D Lehman
Journal:  Radiology       Date:  2017-08       Impact factor: 11.105

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