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Portnow LH, Choridah L, Kardinah K, Handarini T, Pijnappel R, Bluekens AMJ, Duijm LEM, Schoub PK, Smilg PS, Malek L, Leung JWT, Raza S. International Interobserver Variability of Breast Density Assessment. J Am Coll Radiol 2023; 20:671-684. [PMID: 37127220 DOI: 10.1016/j.jacr.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/22/2023] [Accepted: 03/03/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE The aim of this study was to determine variability in visually assessed mammographic breast density categorization among radiologists practicing in Indonesia, the Netherlands, South Africa, and the United States. METHODS Two hundred consecutive 2-D full-field digital screening mammograms obtained from September to December 2017 were selected and retrospectively reviewed from four global locations, for a total of 800 mammograms. Three breast radiologists in each location (team) provided consensus density assessments of all 800 mammograms using BI-RADS® density categorization. Interreader agreement was compared using Gwet's AC2 with quadratic weighting across all four density categories and Gwet's AC1 for binary comparison of combined not dense versus dense categories. Variability of distribution among teams was calculated using the Stuart-Maxwell test of marginal homogeneity across all four categories and using the McNemar test for not dense versus dense categories. To compare readers from a particular country on their own 200 mammograms versus the other three teams, density distribution was calculated using conditional logistic regression. RESULTS For all 800 mammograms, interreader weighted agreement for distribution among four density categories was 0.86 (Gwet's AC2 with quadratic weighting; 95% confidence interval, 0.85-0.88), and for not dense versus dense categories, it was 0.66 (Gwet's AC1; 95% confidence interval, 0.63-0.70). Density distribution across four density categories was significantly different when teams were compared with one another and one team versus the other three teams combined (P < .001). Overall, all readers placed the largest number of mammograms in the scattered and heterogeneous categories. CONCLUSIONS Although reader teams from four different global locations had almost perfect interreader agreement in BI-RADS density categorization, variability in density distribution across four categories remained statistically significant.
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Affiliation(s)
- Leah H Portnow
- Division of Breast Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; and Instructor, Department of Radiology, Harvard Medical School, Boston, Massachusetts.
| | - Lina Choridah
- Vice Dean of Research and Development, Department of Radiology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jalan Farmako, Sekip Utara, Yogyakarta, Indonesia
| | - Kardinah Kardinah
- Director of Early Breast Cancer Detection Program for the Ministry of Health and Medical Committee Leader of Quality Assurance; Department of Radiology, Faculty of Medicine, Dharmais Cancer Hospital/National Cancer Center, Jakarta, Indonesia
| | - Triwulan Handarini
- Chair of the Radiology Medical Staff, Department of Radiology, Faculty of Medicine, Airlangga University-Dr Soetomo Academic General Hospital, Surabaya, Indonesia
| | - Ruud Pijnappel
- Department of Radiology, University Medical Center, Utrecht, the Netherlands; Professor, Utrecht University, Utrecht, the Netherlands; Chair, Dutch Expert Centre for Screening; and President, European Society of Breast Imaging
| | - Adriana M J Bluekens
- Department of Radiology, Elisabeth-TweeSteden Ziekenhuis, Tilburg, the Netherlands
| | - Lucien E M Duijm
- Department of Radiology, Canisius-Wilhelmina Ziekenhuis, Nijmegen, the Netherlands
| | - Peter K Schoub
- Department of Radiology, Parklane Radiology, Johannesburg, South Africa; Chair, Breast Imaging Society of South Africa
| | - Pamela S Smilg
- Department of Radiology, Parklane Radiology, Johannesburg, South Africa; Department of Radiology, Donald Gordon Medical Centre, Johannesburg, South Africa
| | - Liat Malek
- The Breast Wellness Centre, Johannesburg, South Africa
| | - Jessica W T Leung
- Deputy Chair, Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas; and Chair, Ultrasound Subcommittee, BI-RADS Committee, American College of Radiology. https://twitter.com/DrJessicaLeung
| | - Sughra Raza
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Dartmouth Hitchcock Medical Center, Hanover, NH; and Editor-in-Chief, Journal of Global Radiology
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Seitzman RL, Pushkin J, Berg WA. Effect of an Educational Intervention on Women's Health Care Provider Knowledge Gaps About Breast Cancer Risk Model Use and High-risk Screening Recommendations. JOURNAL OF BREAST IMAGING 2023; 5:30-39. [PMID: 38416962 DOI: 10.1093/jbi/wbac072] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Indexed: 03/01/2024]
Abstract
OBJECTIVE To assess effectiveness of a web-based educational intervention on women's health care provider knowledge of breast cancer risk models and high-risk screening recommendations. METHODS A web-based pre- and post-test study including 177 U.S.-based women's health care providers was conducted in 2019. Knowledge gaps were defined as fewer than 75% of respondents answering correctly. Pre- and post-test knowledge differences (McNemar test) and associations of baseline characteristics with pre-test knowledge gaps (logistic regression) were evaluated. RESULTS Respondents included 131/177 (74.0%) physicians; 127/177 (71.8%) practiced obstetrics/gynecology. Pre-test, 118/177 (66.7%) knew the Gail model predicts lifetime invasive breast cancer risk; this knowledge gap persisted post-test [(121/177, 68.4%); P = 0.77]. Just 39.0% (69/177) knew the Gail model identifies women eligible for risk-reducing medications; this knowledge gap resolved. Only 48.6% (86/177) knew the Gail model should not be used to identify women meeting high-risk MRI screening guidelines; this deficiency decreased to 66.1% (117/177) post-test (P = 0.001). Pre-test, 47.5% (84/177) knew the Tyrer-Cuzick model is used to identify women meeting high-risk screening MRI criteria, 42.9% (76/177) to predict BRCA1/2 pathogenic mutation risk, and 26.0% (46/177) to predict lifetime invasive breast cancer risk. These knowledge gaps persisted but improved. For a high-risk 30-year-old, 67.8% (120/177) and 54.2% (96/177) pre-test knew screening MRI and mammography/tomosynthesis are recommended, respectively; 19.2% (34/177) knew both are recommended; and 53% (94/177) knew US is not recommended. These knowledge gaps resolved or reduced. CONCLUSION Web-based education can reduce important provider knowledge gaps about breast cancer risk models and high-risk screening recommendations.
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Affiliation(s)
| | | | - Wendie A Berg
- DenseBreast-info, Inc, Deer Park, NY, USA
- University of Pittsburgh School of Medicine, Magee-Womens Hospital of UPMC, Department of Radiology, Pittsburgh, PA, USA
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Harvey JA. Breast Density and Breast Cancer Risk. JOURNAL OF BREAST IMAGING 2022; 4:339-341. [PMID: 38416985 DOI: 10.1093/jbi/wbac040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Indexed: 03/01/2024]
Affiliation(s)
- Jennifer A Harvey
- University of Rochester Medical Center, Department of Imaging Sciences, Rochester, NY, USA
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