Screening Outcomes of Supplemental Automated Breast US in Asian Women with Dense and Nondense Breasts
Abstract
In Asian women with dense breasts and those with nondense breasts, the addition of automated breast US to digital mammography showed higher cancer detection rates but lower specificities.
Background
Automated breast (AB) US effectively depicts mammographically occult breast cancers in Western women. However, few studies have focused on the outcome of supplemental AB US in Asian women who have denser breasts than Western women.
Purpose
To evaluate the performance of supplemental AB US on mammography-based breast cancer screening in Asian women with dense breasts and those with nondense breasts.
Materials and Methods
A retrospective database search identified asymptomatic Korean women who underwent digital mammography (DM) and supplemental AB US screening for breast cancer between January 2018 and December 2019. We excluded women without sufficient follow-up, established final diagnosis, or histopathologic results. Performance measures of DM alone and AB US combined with DM (hereafter AB US plus DM) were compared. The primary outcome was cancer detection rate (CDR), and the secondary outcomes were sensitivity and specificity. Subgroup analyses were performed based on mammography density.
Results
From 2785 screening examinations in 2301 women (mean age, 52 years ± 9 [SD]), 28 cancers were diagnosed (26 screening-detected cancers, two interval cancers). When compared with DM alone, AB US plus DM resulted in a higher CDR of 9.3 per 1000 examinations (95% CI: 7.7, 10.3) versus 6.5 per 1000 examinations (95% CI: 5.2, 7.2; P < .001) and a higher sensitivity of 90.9% (95% CI: 77.3, 100.0) versus 63.6% (95% CI: 40.9, 81.8; P < .001) but a lower specificity of 86.8% (95% CI: 85.2, 88.2) versus 94.6% (95% CI: 93.6, 95.5; P < .001) in women with dense breasts. In women with nondense breasts, AB US plus DM resulted in a higher CDR of 9.5 per 1000 examinations (95% CI: 7.1, 10.6) versus 6.3 per 1000 examinations (95% CI: 3.5, 7.1; P < .001), whereas specificity was lower at 95.2% (95% CI: 93.4, 96.8) versus 97.1% (95% CI: 95.8, 98.4; P < .001).
Conclusion
In Asian women, the addition of automated breast US to digital mammography showed higher cancer detection rates but lower specificities in both dense and nondense breasts.
© RSNA, 2023
References
- 1. . Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark. Breast Cancer Res 2019;21(1):111. Crossref, Medline, Google Scholar
- 2. . Effect of Digital Mammography for Breast Cancer Screening: A Comparative Study of More than 8 Million Korean Women. Radiology 2020;294(2):247–255. Link, Google Scholar
- 3. . Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 2008;299(18):2151–2163. Crossref, Medline, Google Scholar
- 4. . Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial. Lancet 2016;387(10016):341–348. Crossref, Medline, Google Scholar
- 5. . Screening breast ultrasound: past, present, and future. AJR Am J Roentgenol 2015;204(2):234–240. Crossref, Medline, Google Scholar
- 6. . Operator dependence of physician-performed whole-breast US: lesion detection and characterization. Radiology 2006;241(2):355–365. Link, Google Scholar
- 7. . Automated Breast Ultrasound Screening for Dense Breasts. Korean J Radiol 2020;21(1):15–24. Crossref, Medline, Google Scholar
- 8. . Automated whole breast ultrasound. Radiol Clin North Am 2014;52(3):539–546. Crossref, Medline, Google Scholar
- 9. . Automated Three-dimensional Breast US for Screening: Technique, Artifacts, and Lesion Characterization. RadioGraphics 2018;38(3):663–683. Link, Google Scholar
- 10. . Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use. Acad Radiol 2018;25(11):1457–1470. Crossref, Medline, Google Scholar
- 11. . Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study. Radiology 2015;274(3):663–673. Link, Google Scholar
- 12. . Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 2010;20(3):734–742. Crossref, Medline, Google Scholar
- 13. . Adding 3D automated breast ultrasound to mammography screening in women with heterogeneously and extremely dense breasts: Report from a hospital-based, high-volume, single-center breast cancer screening program. Eur J Radiol 2016;85(9):1554–1563. Crossref, Medline, Google Scholar
- 14. . The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1,886 women. Eur Radiol 2018;28(2):592–601. Crossref, Medline, Google Scholar
- 15. . Screening Breast Ultrasound Using Handheld or Automated Technique in Women with Dense Breasts. J Breast Imaging 2019;1(4):283–296. Crossref, Google Scholar
- 16. . Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007;356(3):227–236. Crossref, Medline, Google Scholar
- 17. . Prevalence of Women with Dense Breasts in Korea: Results from a Nationwide Cross-sectional Study. Cancer Res Treat 2019;51(4):1295–1301. Crossref, Medline, Google Scholar
- 18. . Evaluation of Adjunctive Ultrasonography for Breast Cancer Detection Among Women Aged 40-49 Years With Varying Breast Density Undergoing Screening Mammography: A Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2021;4(8):e2121505. Crossref, Medline, Google Scholar
- 19. . Is Ultrasound an Accurate Alternative for Mammography in Breast Cancer Screening in an Asian Population? A Meta-Analysis. Diagnostics (Basel) 2020;10(11):985. Crossref, Medline, Google Scholar
- 20. . ACR BI-RADS atlas: breast imaging reporting and data system. 5th ed. Reston, Va: American College of Radiology, 2013. Google Scholar
- 21. . An Introduction to the Bootstrap. New York, NY: Chapman & Hall/CRC, 1994. Crossref, Google Scholar
- 22. . Combined screening with mammography and ultrasound in a population-based screening program. Eur J Radiol 2018;101:24–29. Crossref, Medline, Google Scholar
- 23. . The connecticut experiment: the role of ultrasound in the screening of women with dense breasts. Breast J 2012;18(6):517–522. Crossref, Medline, Google Scholar
- 24. . National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiology 2017;283(1):49–58. Link, Google Scholar
- 25. . Automated Breast Ultrasound: Interobserver Agreement, Diagnostic Value, and Associated Clinical Factors of Coronal-Plane Image Features. Korean J Radiol 2020;21(5):550–560. Crossref, Medline, Google Scholar
- 26. . Correlation between three-dimensional ultrasound features and pathological prognostic factors in breast cancer. Eur Radiol 2014;24(6):1186–1196. Crossref, Medline, Google Scholar
- 27. . Mammography and subsequent whole-breast sonography of nonpalpable breast cancers: the importance of radiologic breast density. AJR Am J Roentgenol 2003;180(6):1675–1679. Crossref, Medline, Google Scholar
- 28. . Outcomes of screening mammography by frequency, breast density, and postmenopausal hormone therapy. JAMA Intern Med 2013;173(9):807–816. Crossref, Medline, Google Scholar
- 29. . Effect of age and breast density on screening mammograms with false-positive findings. AJR Am J Roentgenol 1999;173(6):1651–1655. Crossref, Medline, Google Scholar
- 30. . BI-RADS 3 on Screening Breast Ultrasound: What Is It and What Is the Appropriate Management? J Breast Imaging 2021;3(5):527–538. Crossref, Medline, Google Scholar
- 31. . Outcomes of Return to Routine Screening for BI-RADS 3 Lesions Detected at Supplemental Automated Whole-Breast Ultrasound in Women With Dense Breasts: A Prospective Study. AJR Am J Roentgenol 2021;217(6):1313–1321. Crossref, Medline, Google Scholar
Article History
Received: Sept 23 2022Revision requested: Nov 11 2022
Revision received: Feb 20 2023
Accepted: Mar 3 2023
Published online: Apr 25 2023