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Radiology. 2019 Apr;291(1):34-42. doi: 10.1148/radiol.2019182305. Epub 2019 Feb 26.

Digital Breast Tomosynthesis: Radiologist Learning Curve.

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From the Division of Biostatistics, Department of Public Health Sciences, University of California, Davis School of Medicine, One Shields Ave, Med Sci 1C, Room 144, Davis, CA 95616 (D.L.M.); Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Wash (D.L.M., L.A., D.S.M.B.); Department of Radiology, University of Washington School of Medicine; Department of Health Services, University of Washington School of Public Health; Hutchinson Institute for Cancer Outcomes Research, Seattle, Wash (C.I.L.); Department of Radiology (S.D.H.) and Department of Surgery, Office of Health Promotion Research (B.L.S.), Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, Vt; Department of Radiology, University of North Carolina, Chapel Hill, NC (L.M.H.); The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Lebanon, NH (A.N.A.T.); and Departments of Medicine andEpidemiology and Biostatistics and the General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, Calif (K.K.).


Background There is growing evidence that digital breast tomosynthesis (DBT) results in lower recall rates and higher cancer detection rates when compared with digital mammography. However, whether DBT interpretative performance changes with experience (learning curve effect) is unknown. Purpose To evaluate screening DBT performance by cumulative DBT volume within 2 years after adoption relative to digital mammography (DM) performance 1 year before DBT adoption. Materials and Methods This prospective study included 106 126 DBT and 221 248 DM examinations in 271 362 women (mean age, 57.5 years) from 2010 to 2017 that were interpreted by 104 radiologists from 53 facilities in the Breast Cancer Surveillance Consortium. Conditional logistic regression was used to estimate within-radiologist effects of increasing cumulative DBT volume on recall and cancer detection rates relative to DM and was adjusted for examination-level characteristics. Changes were also evaluated by subspecialty and breast density. Results Before DBT adoption, DM recall rate was 10.4% (95% confidence interval [CI]: 9.5%, 11.4%) and cancer detection rate was 4.0 per 1000 screenings (95% CI: 3.6 per 1000 screenings, 4.5 per 1000 screenings); after DBT adoption, DBT recall rate was lower (9.4%; 95% CI: 8.2%, 10.6%; P = .02) and cancer detection rate was similar (4.6 per 1000 screenings; 95% CI: 4.0 per 1000 screenings, 5.2 per 1000 screenings; P = .12). Relative to DM, DBT recall rate decreased for a cumulative DBT volume of fewer than 400 studies (odds ratio [OR] = 0.83; 95% CI: 0.78, 0.89) and remained lower as volume increased (400-799 studies, OR = 0.8 [95% CI: 0.75, 0.85]; 800-1199 studies, OR = 0.81 [95% CI: 0.76, 0.87]; 1200-1599 studies, OR = 0.78 [95% CI: 0.73, 0.84]; 1600-2000 studies, OR = 0.81 [95% CI: 0.75, 0.88]; P < .001). Improvements were sustained for breast imaging subspecialists (OR range, 0.67-0.85; P < .02) and readers who were not breast imaging specialists (OR range, 0.80-0.85; P < .001). Recall rates decreased more in women with nondense breasts (OR range, 0.68-0.76; P < .001) than in those with dense breasts (OR range, 0.86-0.90; P ≤ .05; P interaction < .001). Cancer detection rates for DM and DBT were similar, regardless of DBT volume (P ≥ .10). Conclusion Early performance improvements after digital breast tomosynthesis (DBT) adoption were sustained regardless of DBT volume, radiologist subspecialty, or breast density.

[Available on 2020-04-01]

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