Literature DB >> 15914475

Performance benchmarks for diagnostic mammography.

Edward A Sickles1, Diana L Miglioretti, Rachel Ballard-Barbash, Berta M Geller, Jessica W T Leung, Robert D Rosenberg, Rebecca Smith-Bindman, Bonnie C Yankaskas.   

Abstract

PURPOSE: To evaluate a range of performance parameters pertinent to the comprehensive auditing of diagnostic mammography examinations, and to derive performance benchmarks therefrom, by pooling data collected from large numbers of patients and radiologists that are likely to be representative of mammography practice in the United States.
MATERIALS AND METHODS: Institutional review board approval was met, informed consent was not required, and this study was Health Insurance Portability and Accountability Act compliant. Six mammography registries contributed data to the Breast Cancer Surveillance Consortium (BCSC), providing patient demographic and clinical information, mammogram interpretation data, and biopsy results from defined population-based catchment areas. The study involved 151 mammography facilities and 646 interpreting radiologists. The study population included women 18 years of age or older who underwent at least one diagnostic mammography examination between 1996 and 2001. Collected data were used to derive mean performance parameter values, including abnormal interpretation rate, positive predictive value (for abnormal interpretation, biopsy recommended, and biopsy performed), cancer diagnosis rate, invasive cancer size, and the percentages of minimal cancers, axillary node-negative invasive cancers, and stage 0 and I cancers. Additional benchmarks were derived for these performance parameters, including 10th, 25th, 50th (median), 75th, and 90th percentile values.
RESULTS: The study involved 332,926 diagnostic mammography examinations. Mean performance parameter values were abnormal interpretation rate, 8.0%; positive predictive value for abnormal interpretation, 31.4%; positive predictive value for biopsy recommended, 31.5%; positive predictive value for biopsy performed, 39.5%; cancer diagnosis rate, 25.3 per 1000 examinations; invasive cancer size, 20.2 mm; percentage of minimal cancers, 42.0%; percentage of axillary node-negative invasive cancers, 73.6%; and percentage of stage 0 and I cancers, 62.4%.
CONCLUSION: The presented BCSC outcomes data and performance benchmarks may be used by mammography facilities and individual radiologists to evaluate their own performance for diagnostic mammography as determined by means of periodic comprehensive audits. Copyright RSNA, 2005

Entities:  

Mesh:

Year:  2005        PMID: 15914475     DOI: 10.1148/radiol.2353040738

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


  76 in total

1.  Mammographic interpretive volume and diagnostic mammogram interpretation performance in community practice.

Authors:  Sebastien Haneuse; Diana S M Buist; Diana L Miglioretti; Melissa L Anderson; Patricia A Carney; Tracy Onega; Berta M Geller; Karla Kerlikowske; Robert D Rosenberg; Bonnie C Yankaskas; Joann G Elmore; Stephen H Taplin; Robert A Smith; Edward A Sickles
Journal:  Radiology       Date:  2011-11-21       Impact factor: 11.105

2.  Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer.

Authors:  Hao Jing; Yongyi Yang; Robert M Nishikawa
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

3.  Individualized computer-aided education in mammography based on user modeling: concept and preliminary experiments.

Authors:  Maciej A Mazurowski; Jay A Baker; Huiman X Barnhart; Georgia D Tourassi
Journal:  Med Phys       Date:  2010-03       Impact factor: 4.071

4.  Digital Breast Tomosynthesis: State of the Art.

Authors:  Srinivasan Vedantham; Andrew Karellas; Gopal R Vijayaraghavan; Daniel B Kopans
Journal:  Radiology       Date:  2015-12       Impact factor: 11.105

5.  Radiologists' interpretive skills in screening vs. diagnostic mammography: are they related?

Authors:  Joann G Elmore; Andrea J Cook; Andy Bogart; Patricia A Carney; Berta M Geller; Stephen H Taplin; Diana S M Buist; Tracy Onega; Christoph I Lee; Diana L Miglioretti
Journal:  Clin Imaging       Date:  2016-07-01       Impact factor: 1.605

6.  Bias associated with self-report of prior screening mammography.

Authors:  Kathleen A Cronin; Diana L Miglioretti; Martin Krapcho; Binbing Yu; Berta M Geller; Patricia A Carney; Tracy Onega; Eric J Feuer; Nancy Breen; Rachel Ballard-Barbash
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-06       Impact factor: 4.254

7.  Risk Factors That Increase Risk of Estrogen Receptor-Positive and -Negative Breast Cancer.

Authors:  Karla Kerlikowske; Charlotte C Gard; Jeffrey A Tice; Elad Ziv; Steven R Cummings; Diana L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2016-12-31       Impact factor: 13.506

8.  Assessing the effect of a true-positive recall case in screening mammography: does perceptual priming alter radiologists' performance?

Authors:  S J Lewis; C R Mello-Thoms; P C Brennan; W Lee; A Tan; M F McEntee; M Evanoff; M Pietrzyk; W M Reed
Journal:  Br J Radiol       Date:  2014-05-12       Impact factor: 3.039

9.  Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.

Authors:  Jeffrey A Tice; Diana L Miglioretti; Chin-Shang Li; Celine M Vachon; Charlotte C Gard; Karla Kerlikowske
Journal:  J Clin Oncol       Date:  2015-08-17       Impact factor: 44.544

10.  When radiologists perform best: the learning curve in screening mammogram interpretation.

Authors:  Diana L Miglioretti; Charlotte C Gard; Patricia A Carney; Tracy L Onega; Diana S M Buist; Edward A Sickles; Karla Kerlikowske; Robert D Rosenberg; Bonnie C Yankaskas; Berta M Geller; Joann G Elmore
Journal:  Radiology       Date:  2009-09-29       Impact factor: 11.105

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.