Literature DB >> 16794153

Reality check: perceived versus actual performance of community mammographers.

Joshua J Fenton1, Joseph Egger, Patricia A Carney, Gary Cutter, Carl D'Orsi, Edward A Sickles, Jessica Fosse, Linn Abraham, Stephen H Taplin, William Barlow, R Edward Hendrick, Joann G Elmore.   

Abstract

OBJECTIVE: Federal regulations mandate that radiologists receive regular albeit limited feedback regarding their interpretive accuracy in mammography. We sought to determine whether radiologists who regularly receive more extensive feedback can report their actual performance in screening mammography accurately. SUBJECTS AND METHODS: Radiologists (n = 105) who routinely interpret screening mammograms in three states (Washington, Colorado, and New Hampshire) completed a mailed survey in 2001. Radiologists were asked to estimate how frequently they recommended additional diagnostic testing after screening mammography and the positive predictive value of their recommendations for biopsy (PPV2). We then used outcomes from 336,128 screening mammography examinations interpreted by the radiologists from 1998 to 2001 to ascertain their true rates of recommendations for diagnostic testing and PPV2.
RESULTS: Radiologists' self-reported rate of recommending immediate additional imaging (11.1%) exceeded their actual rate (9.1%) (mean difference, 1.9%; 95% confidence interval [CI], 0.9-3.0%). The mean self-reported rate of recommending short-interval follow-up was 6.2%; the true rate was 1.8% (mean difference, 4.3%; 95% CI, 3.6-5.1%). Similarly, the mean self-reported and true rates of recommending immediate biopsy or surgical evaluation were 3.2% and 0.6%, respectively (mean difference, 2.6%; 95% CI, 1.8-3.4%). Conversely, radiologists' mean self-reported PPV2 (18.3%) was significantly less than their mean true PPV2 (27.6%) (mean difference, -9.3%; 95% CI, -12.4% to -6.2%).
CONCLUSION: Despite regular performance feedback, community radiologists may overestimate their true rates of recommending further evaluation after screening mammography and underestimate their true positive predictive value.

Mesh:

Year:  2006        PMID: 16794153      PMCID: PMC3149896          DOI: 10.2214/AJR.05.0455

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  8 in total

Review 1.  Audit and feedback: effects on professional practice and health care outcomes.

Authors:  G Jamtvedt; J M Young; D T Kristoffersen; M A Thomson O'Brien; A D Oxman
Journal:  Cochrane Database Syst Rev       Date:  2003

2.  Concordance of breast imaging reporting and data system assessments and management recommendations in screening mammography.

Authors:  Stephen H Taplin; Laura E Ichikawa; Karla Kerlikowske; Virginia L Ernster; Robert D Rosenberg; Bonnie C Yankaskas; Patricia A Carney; Berta M Geller; Nicole Urban; Mark B Dignan; William E Barlow; Rachel Ballard-Barbash; Edward A Sickles
Journal:  Radiology       Date:  2002-02       Impact factor: 11.105

3.  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

4.  Do physicians do as they say? The case of mammography.

Authors:  B G Saver; T R Taylor; J R Treadwell; W G Cole
Journal:  Arch Fam Med       Date:  1997 Nov-Dec

5.  Physician intention to recommend complete diagnostic evaluation in colorectal cancer screening.

Authors:  R E Myers; T Hyslop; M Gerrity; N Schlackman; N Hanchak; J Grana; B J Turner; D Weinberg; W W Hauck
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  1999-07       Impact factor: 4.254

6.  Factors associated with implementation of preventive care measures in patients with diabetes mellitus.

Authors:  D A Streja; S W Rabkin
Journal:  Arch Intern Med       Date:  1999-02-08

7.  Comparison of screening mammography in the United States and the United kingdom.

Authors:  Rebecca Smith-Bindman; Philip W Chu; Diana L Miglioretti; Edward A Sickles; Roger Blanks; Rachel Ballard-Barbash; Janet K Bobo; Nancy C Lee; Matthew G Wallis; Julietta Patnick; Karla Kerlikowske
Journal:  JAMA       Date:  2003-10-22       Impact factor: 56.272

8.  International variation in screening mammography interpretations in community-based programs.

Authors:  Joann G Elmore; Connie Y Nakano; Thomas D Koepsell; Laurel M Desnick; Carl J D'Orsi; David F Ransohoff
Journal:  J Natl Cancer Inst       Date:  2003-09-17       Impact factor: 13.506

  8 in total
  20 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.  Use of clinical history affects accuracy of interpretive performance of screening mammography.

Authors:  Patricia A Carney; Andrea J Cook; Diana L Miglioretti; Stephen A Feig; Erin Aiello Bowles; Berta M Geller; Karla Kerlikowske; Mark Kettler; Tracy Onega; Joann G Elmore
Journal:  J Clin Epidemiol       Date:  2011-10-15       Impact factor: 6.437

3.  Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.

Authors:  Maxine Tan; Bin Zheng; Pandiyarajan Ramalingam; David Gur
Journal:  Acad Radiol       Date:  2013-12       Impact factor: 3.173

4.  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

5.  Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method.

Authors:  Xingwei Wang; Lihua Li; Weidong Xu; Wei Liu; Dror Lederman; Bin Zheng
Journal:  Phys Med Biol       Date:  2012-01-21       Impact factor: 3.609

6.  A preliminary evaluation of multi-probe resonance-frequency electrical impedance based measurements of the breast.

Authors:  Bin Zheng; Dror Lederman; Jules H Sumkin; Margarita L Zuley; Michelle Z Gruss; Linda S Lovy; David Gur
Journal:  Acad Radiol       Date:  2010-12-03       Impact factor: 3.173

7.  Association between computed tissue density asymmetry in bilateral mammograms and near-term breast cancer risk.

Authors:  Bin Zheng; Maxine Tan; Pandiyarajan Ramalingam; David Gur
Journal:  Breast J       Date:  2014-03-27       Impact factor: 2.431

8.  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

9.  Detection of breast abnormalities using a prototype resonance electrical impedance spectroscopy system: a preliminary study.

Authors:  Bin Zheng; Margarita L Zuley; Jules H Sumkin; Victor J Catullo; Gordon S Abrams; Grace Y Rathfon; Denise M Chough; Michelle Z Gruss; David Gur
Journal:  Med Phys       Date:  2008-07       Impact factor: 4.071

10.  Mammographic interpretation: radiologists' ability to accurately estimate their performance and compare it with that of their peers.

Authors:  Andrea J Cook; Joann G Elmore; Weiwei Zhu; Sara L Jackson; Patricia A Carney; Chris Flowers; Tracy Onega; Berta Geller; Robert D Rosenberg; Diana L Miglioretti
Journal:  AJR Am J Roentgenol       Date:  2012-09       Impact factor: 3.959

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