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Comparison between execution and reading time of 3D ABUS versus HHUS

  • BREAST RADIOLOGY
  • Published:
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Abstract

Background

Breast density is an independent risk factor for breast cancer. Mammography is supplemented with handheld ultrasound (HHUS) to increase sensitivity. Automatic breast ultrasound (ABUS) is an alternative to HHUS. Our study wanted to assess the difference in execution and reading time between ABUS and HHUS.

Methods and materials

N = 221 women were evaluated consecutively between January 2019 and June 2019 (average age 53 years; range 24–89). The execution and reading time of ABUS and HHUS was calculated with an available stopwatch. Time started for both procedures when the patient was ready on the examination table to be examined to the end of image acquisition and interpretation.

Results

No patients interrupted the exam due to pain or discomfort. N = 221 women underwent ABUS and HHUS; N = 11 patients refused to undergo both procedures due to time constraints and refused ABUS; therefore, 210 patients were enrolled with both ABUS and HHUS available. The average time to perform and read the exam was 5 min for HHUS (DS ± 1.5) with a maximum time of 11 min and a minimum of 2 min. The average time with ABUS was 17 min (DS ± 3.8, with a maximum time of 31 min and a minimum time of 9 min). The ABUS technique took longer to be performed in all patients, with an average difference of 11 min (range 3–23 min) per patient, P < 0,001. Separating ABUS execution from reading time we highlighted as ABUS execution is more time-consuming respect HHUS. In addition, we can underline that time required by radiologists is longer for ABUS even only considering the interpretation time of the exam.

Conclusion

A significant difference was observed in the execution and reading time of the two exams, where the HHUS method was more rapid and tolerated.

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References

  1. Harvey JA, Bovbjerg VE (2004) Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 230(1):29–41

    Article  Google Scholar 

  2. Ciatto S, Visioli C, Paci E, Zappa M (2004) Breast density as a determinant of interval cancer at mammographic screening. Br Cancer 90(2):393–396

    Article  CAS  Google Scholar 

  3. Tagliafico AS, Mariscotti G, Valdora F, Durando M, Nori J, La Forgia D, Rosenberg I, Caumo F, Gandolfo N, Sormani MP, Signori A, Calabrese M, Houssami N (2018) A prospective comparative trial of adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts (ASTOUND-2). Eur J Cancer 104:39–46

    Article  Google Scholar 

  4. Tagliafico AS, Calabrese M, Mariscotti G, Durando M, Tosto S, Monetti F, Airaldi S, Bignotti B, Nori J, Bagni A, Signori A, Sormani MP, Houssami N (2016) Adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts: interim report of a prospective comparative trial. J Clin Oncol 34(16):1882–1888

    Article  CAS  Google Scholar 

  5. Harbeck N, Penault-Llorca F, Cortes J, Gnant M, Houssami N, Poortmans P, Ruddy K, Tsang J, Cardoso F (2019) Breast cancer. Nat Rev Dis Primers 5(1):66

    Article  Google Scholar 

  6. Houssami N, Lee CI (2018) The impact of legislation mandating breast density notification—review of the evidence. Breast 42:102–112

    Article  Google Scholar 

  7. Houssami N, Cho N (2018) Screening women with a personal history of breast cancer: overview of the evidence on breast imaging surveillance. Ultrasonography 37(4):277–287

    Article  Google Scholar 

  8. Phi XA, Tagliafico A, Houssami N, Greuter MJW, deBock GH (2018) Digital breast tomosynthesis for breast cancer screening and diagnosis in women with dense breasts: a systematic review and meta-analysis. BMC Cancer 18(1):380

    Article  Google Scholar 

  9. U.S. Food and Drug Administration Medical devices: somo-v Automated Breast Ultrasound System (ABUS): P110006 [Internet]. Silver Spring, MD: U.S. Food and Drug Administration. 2012 [cited 2014 Apr 10]. http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/DeviceApprovalsandClearances/Recently-ApprovedDevices/ucm320724.htm

  10. Wilczek B, Wilczek HE, Rasouliyan L, Leifland K (2016) 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 85(9):1554–1563

    Article  Google Scholar 

  11. Kim SH, Kang BJ, Choi BG, Choi JJ, Lee JH, Song BJ, Choe BJ, Park S, Kim H (2013) Radiologists’ performance for detecting lesions and the interobserver variability of automated whole breast ultrasound. Korean J Radiol 14(2):154–163

    Article  Google Scholar 

  12. Shin HJ, Kim HH, Cha JH, Park JH, Lee KE, Kim JH (2011) Automated ultrasound of the breast for diagnosis: interobserver agreement on lesion detection and characterization. Am J Roentgenol 197(3):747–754

    Article  Google Scholar 

  13. Vourtsis A, Kachulis A (2018) The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 1886 women. Eur Radiol 28:592–601

    Article  Google Scholar 

  14. Mundinger A (2016) 3D supine automated ultrasound (saus, abus, abvs) for supplemental screening women with dense breast. Eur J Breast Health 12(2):52–55

    Article  Google Scholar 

  15. Kotsianos-Hermle D, Hiltawsky KM, Wirth S, Fischer T, Friese K, Reiser M (2009) Analysis of 107 breast lesions with automated 3D ultrasound and comparison with mammography and manual ultrasound. Eur J Radiol 71:109–115

    Article  CAS  Google Scholar 

  16. American College of Radiology (2013) ACR BI-RADS Atlas®, 5th edn

  17. Madjar H, Mendelson EB (2008) The practice of breast ultrasound. Thieme, New York

    Book  Google Scholar 

  18. Berg Wendie A (2016) Current status of supplemental screening in dense breasts. J Clin Oncol 34(16):1840–1843

    Article  Google Scholar 

  19. Arslan A, Ertaş G, Arıbal E (2019) 3D Automated breast ultrasound system: comparison of interpretation time of senior versus junior radiologist. Eur J Breast Health 15(3):153–157

    Article  Google Scholar 

  20. Huppe AI, Inciardi MF, Redick M, Carroll M, Buckley J, Hill JD, Gatewood JB (2018) Automated breast ultrasound interpretation times: a reader performance study. Acad Radiol 25(12):1577–1581

    Article  Google Scholar 

  21. Miglioretti DL, Gard CC, Carney PA, Onega TL, Buist DS, Sickles EA, Kerlikowske K, Rosenberg RD, Yankaskas BC, Geller BM, Elmore JG (2009) When radiologists perform best: the learning curve in screening mammography interpretation. Radiology 253:3

    Article  Google Scholar 

  22. Skaane P, Gullien R, Eben EB, Sandhaug M, Schulz-Wendtland R, Stoeblen F (2015) Interpretation of automated breast ultrasound (ABUS) with and without knowledge of mammography: a reader performance study. Acta Radiol 56:404–412

    Article  Google Scholar 

  23. Lin X, Wang J, Han F, Fu J, Li A (2012) Analysis of eighty-one cases with breast lesions using automated breast volume scanner and comparison with handheld ultrasound. Eur J Radiol 81:873–878

    Article  Google Scholar 

  24. Brem RF, Tabár L, Duffy SW, Inciardi MF, Guingrich JA, Hashimoto BE, Lander MR, Lapidus RL, Peterson MK, Rapelyea JA, Roux S, Schilling KJ, Shah BA, Torrente J, Wynn RT, Miller DP (2014) Assessing improvement in detection of breast cancer with three-dimensional automated breast US in women with dense breast tissue: the SomoInsight Study. Radiology 274:663–673

    Article  Google Scholar 

  25. Jiang Y, Inciardi MF, Edwards AV, Papaioannou J (2018) Interpretation time using a concurrent-read computer-aided detection system for automated breast ultrasound in breast cancer screening of women with dense breast tissue. Am J Roentgenol 211:452–461. https://doi.org/10.2214/AJR.18.19516

    Article  Google Scholar 

  26. Tagliafico A. Tomosynthesis (TS) or ultrasound (US) in mammography-negative dense breasts (TOMUS). National Multicenter Trial (TOMUS). https://clinicaltrials.gov/ct2/show/study/NCT03033030

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Health Sciences (DISSAL)- Radiology Section, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy

    Nicole Brunetti, Sara De Giorgis, Jeries Zawaideh, Federica Rossi & Alberto Stefano Tagliafico

  2. IRCCS - Ospedale Policlinico San Martino, Largo Rosanna Benzi. 10, 16132, Genoa, Italy

    Massimo Calabrese & Alberto Stefano Tagliafico

Authors
  1. Nicole Brunetti
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  2. Sara De Giorgis
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  3. Jeries Zawaideh
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  4. Federica Rossi
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  5. Massimo Calabrese
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  6. Alberto Stefano Tagliafico
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Correspondence to Nicole Brunetti.

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The authors have no personal, financial or institutional interest with regards to the authorship and/or publication of this manuscript.

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This research involving human participants has been approved by the Ethics Committee.

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The study was approved by the Ethics Committee, and informed consent was obtained from all participating patients (102REG2016).

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Brunetti, N., De Giorgis, S., Zawaideh, J. et al. Comparison between execution and reading time of 3D ABUS versus HHUS. Radiol med 125, 1243–1248 (2020). https://doi.org/10.1007/s11547-020-01209-8

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  • DOI: https://doi.org/10.1007/s11547-020-01209-8

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