Literature DB >> 28244803

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

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

Abstract

Purpose To establish contemporary performance benchmarks for diagnostic digital mammography with use of recent data from the Breast Cancer Surveillance Consortium (BCSC). Materials and Methods Institutional review board approval was obtained for active or passive consenting processes or to obtain a waiver of consent to enroll participants, link data, and perform analyses. Data were obtained from six BCSC registries (418 radiologists, 92 radiology facilities). Mammogram indication and assessments were prospectively collected for women undergoing diagnostic digital mammography and linked with cancer diagnoses from state cancer registries. The study included 401 548 examinations conducted from 2007 to 2013 in 265 360 women. Results Overall diagnostic performance measures were as follows: cancer detection rate, 34.7 per 1000 (95% confidence interval [CI]: 34.1, 35.2); abnormal interpretation rate, 12.6% (95% CI: 12.5%, 12.7%); positive predictive value (PPV) of a biopsy recommendation (PPV2), 27.5% (95% CI: 27.1%, 27.9%); PPV of biopsies performed (PPV3), 30.4% (95% CI: 29.9%, 30.9%); false-negative rate, 4.8 per 1000 (95% CI: 4.6, 5.0); sensitivity, 87.8% (95% CI: 87.3%, 88.4%); and specificity, 90.5% (95% CI: 90.4%, 90.6%). Among cancers detected, 63.4% were stage 0 or 1 cancers, 45.6% were minimal cancers, the mean size of invasive cancers was 21.2 mm, and 69.6% of invasive cancers were node negative. Performance metrics varied widely across diagnostic indications, with cancer detection rate (64.5 per 1000) and abnormal interpretation rate (18.7%) highest for diagnostic mammograms obtained to evaluate a breast problem with a lump. Compared with performance during the screen-film mammography era, diagnostic digital performance showed increased abnormal interpretation and cancer detection rates and decreasing PPVs, with less than 70% of radiologists within acceptable ranges for PPV2 and PPV3. Conclusion These performance measures can serve as national benchmarks that may help transform the marked variation in radiologists' diagnostic performance into targeted quality improvement efforts. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28244803      PMCID: PMC5375630          DOI: 10.1148/radiol.2017161519

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


  13 in total

1.  The Mammography Quality Standards Act. An overview of the regulations and guidance.

Authors:  B S Monsees
Journal:  Radiol Clin North Am       Date:  2000-07       Impact factor: 2.303

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 and the challenges of implementing an emerging breast cancer screening technology into clinical practice.

Authors:  Christoph I Lee; Constance D Lehman
Journal:  J Am Coll Radiol       Date:  2013-12       Impact factor: 5.532

4.  Comparing the performance of mammography screening in the USA and the UK.

Authors:  Rebecca Smith-Bindman; Rachel Ballard-Barbash; Diana L Miglioretti; Julietta Patnick; Karla Kerlikowske
Journal:  J Med Screen       Date:  2005       Impact factor: 2.136

5.  Breast Cancer Surveillance Consortium: a national mammography screening and outcomes database.

Authors:  R Ballard-Barbash; S H Taplin; B C Yankaskas; V L Ernster; R D Rosenberg; P A Carney; W E Barlow; B M Geller; K Kerlikowske; B K Edwards; C F Lynch; N Urban; C A Chrvala; C R Key; S P Poplack; J K Worden; L G Kessler
Journal:  AJR Am J Roentgenol       Date:  1997-10       Impact factor: 3.959

6.  Diagnostic mammography: identifying minimally acceptable interpretive performance criteria.

Authors:  Patricia A Carney; Jay Parikh; Edward A Sickles; Stephen A Feig; Barbara Monsees; Lawrence W Bassett; Robert A Smith; Robert Rosenberg; Laura Ichikawa; James Wallace; Khai Tran; Diana L Miglioretti
Journal:  Radiology       Date:  2013-01-07       Impact factor: 11.105

7.  Sensitivity and specificity of mammographic screening as practised in Vermont and Norway.

Authors:  S Hofvind; B M Geller; J Skelly; P M Vacek
Journal:  Br J Radiol       Date:  2012-09-19       Impact factor: 3.039

8.  Medical audit of diagnostic mammography examinations: comparison with screening outcomes obtained concurrently.

Authors:  K E Dee; E A Sickles
Journal:  AJR Am J Roentgenol       Date:  2001-03       Impact factor: 3.959

9.  Performance benchmarks for diagnostic mammography.

Authors:  Edward A Sickles; Diana L Miglioretti; Rachel Ballard-Barbash; Berta M Geller; Jessica W T Leung; Robert D Rosenberg; Rebecca Smith-Bindman; Bonnie C Yankaskas
Journal:  Radiology       Date:  2005-06       Impact factor: 11.105

10.  Accuracy of short-interval follow-up mammograms by patient and radiologist characteristics.

Authors:  Erin J Aiello Bowles; Diana L Miglioretti; Edward A Sickles; Linn Abraham; Patricia A Carney; Bonnie C Yankaskas; Joann G Elmore
Journal:  AJR Am J Roentgenol       Date:  2008-05       Impact factor: 3.959

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  29 in total

1.  Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Authors:  Jennifer C Ginestra; Heather M Giannini; William D Schweickert; Laurie Meadows; Michael J Lynch; Kimberly Pavan; Corey J Chivers; Michael Draugelis; Patrick J Donnelly; Barry D Fuchs; Craig A Umscheid
Journal:  Crit Care Med       Date:  2019-11       Impact factor: 7.598

2.  Classification of breast microcalcifications using dual-energy mammography.

Authors:  Bahaa Ghammraoui; Andrey Makeev; Ahmed Zidan; Alaadin Alayoubi; Stephen J Glick
Journal:  J Med Imaging (Bellingham)       Date:  2019-03-12

Review 3.  A review of optical breast imaging: Multi-modality systems for breast cancer diagnosis.

Authors:  Quing Zhu; Steven Poplack
Journal:  Eur J Radiol       Date:  2020-05-18       Impact factor: 3.528

4.  Establishment of Best Practices for Evidence for Prediction: A Review.

Authors:  Russell A Poldrack; Grace Huckins; Gael Varoquaux
Journal:  JAMA Psychiatry       Date:  2020-05-01       Impact factor: 21.596

5.  Breast cancer early detection: A phased approach to implementation.

Authors:  Ophira Ginsburg; Cheng-Har Yip; Ari Brooks; Anna Cabanes; Maira Caleffi; Jorge Antonio Dunstan Yataco; Bishal Gyawali; Valerie McCormack; Myrna McLaughlin de Anderson; Ravi Mehrotra; Alejandro Mohar; Raul Murillo; Lydia E Pace; Electra D Paskett; Anya Romanoff; Anne F Rositch; John R Scheel; Miriam Schneidman; Karla Unger-Saldaña; Verna Vanderpuye; Tsu-Yin Wu; Safina Yuma; Allison Dvaladze; Catherine Duggan; Benjamin O Anderson
Journal:  Cancer       Date:  2020-05-15       Impact factor: 6.860

6.  Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

Authors:  Xin Li; Genggeng Qin; Qiang He; Lei Sun; Hui Zeng; Zilong He; Weiguo Chen; Xin Zhen; Linghong Zhou
Journal:  Eur Radiol       Date:  2019-11-05       Impact factor: 5.315

7.  Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis.

Authors:  Benjamin Hinton; Lin Ma; Amir Pasha Mahmoudzadeh; Serghei Malkov; Bo Fan; Heather Greenwood; Bonnie Joe; Vivian Lee; Fredrik Strand; Karla Kerlikowske; John Shepherd
Journal:  Med Phys       Date:  2019-02-14       Impact factor: 4.071

8.  The Value of Outpatient Imaging-Based Cancer Screening Episodes.

Authors:  Joshua M Liao; Anirban Basu; Christoph I Lee
Journal:  J Gen Intern Med       Date:  2018-07-18       Impact factor: 5.128

9.  Incorporating Baseline Breast Density When Screening Women at Average Risk for Breast Cancer : A Cost-Effectiveness Analysis.

Authors:  Ya-Chen Tina Shih; Wenli Dong; Ying Xu; Ruth Etzioni; Yu Shen
Journal:  Ann Intern Med       Date:  2021-02-09       Impact factor: 25.391

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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