Christoph I Lee1, Laura Ichikawa2, Michele C Rochelle3, Karla Kerlikowske4, Diana L Miglioretti5, Brian L Sprague6, Wendy B DeMartini7, Karen J Wernli2, Bonnie N Joe8, Bonnie C Yankaskas9, Constance D Lehman3. 1. Department of Radiology, University of Washington School of Medicine, 825 Eastlake Ave East, G3-200, Seattle, WA, 98109-1023; Department of Health Services, University of Washington School of Public Health, Seattle, Washington. Electronic address: stophlee@uw.edu. 2. Group Health Research Institute, Seattle, Washington. 3. Department of Radiology, University of Washington School of Medicine, 825 Eastlake Ave East, G3-200, Seattle, WA, 98109-1023. 4. General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, San Francisco, California; Department of Medicine, University of California, San Francisco, San Francisco, California; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California. 5. Group Health Research Institute, Seattle, Washington; Department of Public Health Sciences, University of California, Davis, Davis, California. 6. Department of Surgery and Office of Health Promotion Research, University of Vermont, Burlington, Vermont. 7. Department of Radiology, University of Wisconsin School of Medicine, Madison, Wisconsin. 8. Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California. 9. Department of Radiology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina; Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina.
Abstract
RATIONALE AND OBJECTIVES: As breast magnetic resonance imaging (MRI) use grows, benchmark performance parameters are needed for auditing and quality assurance purposes. We describe the variation in breast MRI abnormal interpretation rates (AIRs) by clinical indication among a large sample of US community practices. MATERIALS AND METHODS: We analyzed data from 41 facilities across five Breast Cancer Surveillance Consortium imaging registries. Each registry obtained institutional review board approval for this Health Insurance Portability and Accountability Act compliant analysis. We included 11,654 breast MRI examinations conducted in 2005-2010 among women aged 18-79 years. We categorized clinical indications as 1) screening, 2) extent of disease, 3) diagnostic (eg, breast symptoms), and 4) other (eg, short-interval follow-up). We characterized assessments as positive (ie, Breast Imaging Reporting and Data System [BI-RADS] 0, 4, and 5) or negative (ie, BI-RADS 1, 2, and 6) and provide results with BI-RADS 3 categorized as positive and negative. We tested for differences in AIRs across clinical indications both unadjusted and adjusted for patient characteristics and registry and assessed for changes in AIRs by year within each clinical indication. RESULTS: When categorizing BI-RADS 3 as positive, AIRs were 21.0% (95% confidence interval [CI], 19.8-22.3) for screening, 31.7% (95% CI, 29.6-33.8) for extent of disease, 29.7% (95% CI, 28.3-31.1) for diagnostic, and 27.4% (95% CI, 25.0-29.8) for other indications (P < .0001). When categorizing BI-RADS 3 as negative, AIRs were 10.5% (95% CI, 9.5-11.4) for screening, 21.8% (95% CI, 19.9-23.6) for extent of disease, 17.7% (95% CI, 16.5-18.8) for diagnostic, and 13.3% (95% CI, 11.6-15.2) for other indications (P < .0001). The significant differences in AIRs by indication persisted even after adjusting for patient characteristics and registry (P < .0001). In addition, for most indications, there were no significant changes in AIRs over time. CONCLUSIONS: Breast MRI AIRs differ significantly by clinical indication. Practices should stratify breast MRI examinations by indication for quality assurance and auditing purposes.
RATIONALE AND OBJECTIVES: As breast magnetic resonance imaging (MRI) use grows, benchmark performance parameters are needed for auditing and quality assurance purposes. We describe the variation in breast MRI abnormal interpretation rates (AIRs) by clinical indication among a large sample of US community practices. MATERIALS AND METHODS: We analyzed data from 41 facilities across five Breast Cancer Surveillance Consortium imaging registries. Each registry obtained institutional review board approval for this Health Insurance Portability and Accountability Act compliant analysis. We included 11,654 breast MRI examinations conducted in 2005-2010 among women aged 18-79 years. We categorized clinical indications as 1) screening, 2) extent of disease, 3) diagnostic (eg, breast symptoms), and 4) other (eg, short-interval follow-up). We characterized assessments as positive (ie, Breast Imaging Reporting and Data System [BI-RADS] 0, 4, and 5) or negative (ie, BI-RADS 1, 2, and 6) and provide results with BI-RADS 3 categorized as positive and negative. We tested for differences in AIRs across clinical indications both unadjusted and adjusted for patient characteristics and registry and assessed for changes in AIRs by year within each clinical indication. RESULTS: When categorizing BI-RADS 3 as positive, AIRs were 21.0% (95% confidence interval [CI], 19.8-22.3) for screening, 31.7% (95% CI, 29.6-33.8) for extent of disease, 29.7% (95% CI, 28.3-31.1) for diagnostic, and 27.4% (95% CI, 25.0-29.8) for other indications (P < .0001). When categorizing BI-RADS 3 as negative, AIRs were 10.5% (95% CI, 9.5-11.4) for screening, 21.8% (95% CI, 19.9-23.6) for extent of disease, 17.7% (95% CI, 16.5-18.8) for diagnostic, and 13.3% (95% CI, 11.6-15.2) for other indications (P < .0001). The significant differences in AIRs by indication persisted even after adjusting for patient characteristics and registry (P < .0001). In addition, for most indications, there were no significant changes in AIRs over time. CONCLUSIONS: Breast MRI AIRs differ significantly by clinical indication. Practices should stratify breast MRI examinations by indication for quality assurance and auditing purposes.
Authors: Berta M Geller; William E Barlow; Rachel Ballard-Barbash; Virginia L Ernster; Bonnie C Yankaskas; Edward A Sickles; Patricia A Carney; Mark B Dignan; Robert D Rosenberg; Nicole Urban; Yingye Zheng; Stephen H Taplin Journal: Radiology Date: 2002-02 Impact factor: 11.105
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
Authors: Ruth M L Warren; Linda Pointon; Rebecca Caines; Carmel Hayes; Deborah Thompson; Martin O Leach Journal: Magn Reson Imaging Date: 2002-09 Impact factor: 2.546
Authors: P A Carney; B M Geller; H Moffett; M Ganger; M Sewell; W E Barlow; N Stalnaker; S H Taplin; C Sisk; V L Ernster; H A Wilkie; B Yankaskas; S P Poplack; N Urban; M M West; R D Rosenberg; S Michael; T D Mercurio; R Ballard-Barbash Journal: Am J Epidemiol Date: 2000-08-15 Impact factor: 4.897
Authors: Julie E Weiss; Martha Goodrich; Kimberly A Harris; Rachael E Chicoine; Marie B Synnestvedt; Steve J Pyle; Jane S Chen; Sally D Herschorn; Elisabeth F Beaber; Jennifer S Haas; Anna N A Tosteson; Tracy Onega Journal: J Am Coll Radiol Date: 2016-10-13 Impact factor: 5.532
Authors: Roberta M Strigel; Jennifer Rollenhagen; Elizabeth S Burnside; Mai Elezaby; Amy M Fowler; Frederick Kelcz; Lonie Salkowski; Wendy B DeMartini Journal: Acad Radiol Date: 2016-12-13 Impact factor: 3.173
Authors: Paola Clauser; Barbara Krug; Hubert Bickel; Matthias Dietzel; Katja Pinker; Victor-Frederic Neuhaus; Maria Adele Marino; Marco Moschetta; Nicoletta Troiano; Thomas H Helbich; Pascal A T Baltzer Journal: Clin Cancer Res Date: 2021-01-14 Impact factor: 12.531
Authors: Sarah Eskreis-Winkler; Katherine Simon; Melissa Reichman; Pascal Spincemaille; Thanh D Nguyen; Paul J Christos; Michele Drotman; Martin R Prince; Katja Pinker; Elizabeth J Sutton; Elizabeth A Morris; Yi Wang Journal: Front Oncol Date: 2021-03-22 Impact factor: 6.244