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Automated breast ultrasound: basic principles and emerging clinical applications

  • BREAST RADIOLOGY
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Abstract

Automated breast ultrasound (ABUS) is a recently introduced ultrasonography technique, developed with the purpose to standardize breast ultrasonography and overcome some limitations of handheld ultrasound (HHUS), such as operator dependence and the considerable amount of medical time necessary to perform and interpret HHUS. This new ultrasonography technique separates the moment of image acquisition (that may be performed also by a technician) from that of its interpretation, increasing reproducibility, reducing operator-dependence and physician time. Moreover, multiplanar reconstructions, especially the coronal view, introduce new diagnostic information. ABUS, with those advantages, has the potential to be used as an adjunctive tool to screening mammography, especially in the dense breast, where mammography has a relatively low sensitivity. Women’s awareness of risks related to breast density is a hot topic, especially in the USA where legislative breast density notification laws increase the demand for supplemental ultrasound screening. Therefore, ABUS might have the potential to respond to this need. The purpose of this article is to present a summary of current state-of-the-art of ABUS technology and applications, with an emphasis on breast cancer screening. This article discusses also how to overcome some ABUS limitations, in order to be familiar with the new technique.

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References

  1. Brem RF, Lenihan MJ, Lieberman J, Torrente J (2015) Screening breast ultrasound: past, present, and future. AJR Am J Roentgenol 204:234–240

    Article  PubMed  Google Scholar 

  2. Tabár L, Fagerberg CJ, Gad A et al (1985) Reduction in mortality from breast cancer after mass screening with mammography. Randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare. Lancet 1:829–832

    Article  PubMed  Google Scholar 

  3. Kolb TM, Lichy J, Newhouse JH (2002) Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology 225:165–175

    Article  PubMed  Google Scholar 

  4. Kaplan SS (2014) Automated whole breast ultrasound. Radiol Clin N Am 52:539–546

    Article  PubMed  Google Scholar 

  5. Berg WA, Blume JD, Cormack JB et al (2008) Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA 299:2151–2163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kolb TM, Lichy J, Newhouse JH (1998) Occult cancer in women with dense breasts: detection with screening US–diagnostic yield and tumor characteristics. Radiology 207:191–199

    Article  CAS  PubMed  Google Scholar 

  7. Kaplan SS (2001) Clinical utility of bilateral whole-breast US in the evaluation of women with dense breast tissue. Radiology 221:641–649

    Article  CAS  PubMed  Google Scholar 

  8. Buchberger W, Niehoff A, Obrist P, DeKoekkoek-Doll P, Dünser M (2000) Clinically and mammographically occult breast lesions: detection and classification with high-resolution sonography. Semin Ultrasound CT MR 21:325–336

    Article  CAS  PubMed  Google Scholar 

  9. Gordon PB, Goldenberg SL (1995) Malignant breast masses detected only by ultrasound. A retrospective review. Cancer 76:626–630

    Article  CAS  PubMed  Google Scholar 

  10. Girometti R, Zanotel M, Londero V, Bazzocchi M, Zuiani C (2017) Comparison between automated breast volume scanner (ABVS) versus hand-held ultrasound as a second look procedure after magnetic resonance imaging. Eur Radiol 27:3767–3775

    Article  PubMed  Google Scholar 

  11. Jackson VP, Kelly-Fry E, Rothschild PA, Holden RW, Clark SA (1986) Automated breast sonography using a 7.5-MHz PVDF transducer: preliminary clinical evaluation. Work in progress. Radiology 159:679–684

    Article  CAS  PubMed  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. AJR Am J Roentgenol 197:747–754

    Article  PubMed  Google Scholar 

  13. Kelly KM, Dean J, Comulada WS, Lee SJ (2010) Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. Eur Radiol 20:734–742

    Article  PubMed  Google Scholar 

  14. 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 85:1554–1563

    Article  PubMed  Google Scholar 

  15. Brem RF, Tabár L, Duffy SW et al (2015) 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  PubMed  Google Scholar 

  16. Chang JM, Cha JH, Park JS, Kim SJ, Moon WK (2015) Automated breast ultrasound system (ABUS): reproducibility of mass localization, size measurement, and characterization on serial examinations. Acta Radiol 56:1163–1170

    Article  PubMed  Google Scholar 

  17. Xu C, Wei S, Xie Y, Guan X, Yang B (2016) Three-dimensional assessment of automated breast volume scanner compared with handheld ultrasound in pre-operative breast invasive ductal carcinomas: a pilot study of 51 cases. Ultrasound Med Biol 42:2089–2096

    Article  PubMed  Google Scholar 

  18. Li N, Jiang YX, Zhu QL et al (2013) Accuracy of an automated breast volume ultrasound system for assessment of the pre-operative extent of pure ductal carcinoma in situ: comparison with a conventional handheld ultrasound examination. Ultrasound Med Biol 39:2255–2263

    Article  PubMed  Google Scholar 

  19. Tozaki M, Fukuma E (2010) Accuracy of determining preoperative cancer extent measured by automated breast ultrasonography. Jpn J Radiol 28:771–773

    Article  PubMed  Google Scholar 

  20. Shin HJ, Kim HH, Cha JH (2015) Current status of automated breast ultrasonography. Ultrasonography 34:165–172

    Article  PubMed  PubMed Central  Google Scholar 

  21. Gazhonova V (2017) 3D automated breast volume sonography. Springer, Cham

    Book  Google Scholar 

  22. Durand MA, Hooley RJ (2017) Implementation of whole-breast screening ultrasonography. Radiol Clin N Am 55:527–539

    Article  PubMed  Google Scholar 

  23. Wang HY, Jiang YX, Zhu QL et al (2012) Differentiation of benign and malignant breast lesions: a comparison between automatically generated breast volume scans and handheld ultrasound examinations. Eur J Radiol 81:3190–3200

    Article  PubMed  Google Scholar 

  24. OʼFlynn EAM, Fromageau J, Ledger AE et al (2017) Ultrasound tomography evaluation of breast density: a comparison with noncontrast magnetic resonance imaging. Investig Radiol 52:343–348

    Article  Google Scholar 

  25. Kelly KM, Dean J, Lee SJ, Comulada WS (2010) Breast cancer detection: radiologists’ performance using mammography with and without automated whole-breast ultrasound. Eur Radiol 20:2557–2564

    Article  PubMed  PubMed Central  Google Scholar 

  26. 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  PubMed  Google Scholar 

  27. Chang JM, Moon WK, Cho N et al (2011) Breast cancers initially detected by hand-held ultrasound: detection performance of radiologists using automated breast ultrasound data. Acta Radiol 52:8–14

    Article  PubMed  Google Scholar 

  28. Chang JM, Moon WK, Cho N, Park JS, Kim SJ (2011) Radiologists’ performance in the detection of benign and malignant masses with 3D automated breast ultrasound (ABUS). Eur J Radiol 78:99–103

    Article  PubMed  Google Scholar 

  29. Choi WJ, Cha JH, Kim HH et al (2014) Comparison of automated breast volume scanning and hand-held ultrasound in the detection of breast cancer: an analysis of 5,566 patients evaluations. Asian Pac J Cancer Prev 15:9101–9105

    Article  PubMed  Google Scholar 

  30. Wojcinski S, Gyapong S, Farrokh A, Soergel P, Hillemanns P, Degenhardt F (2013) Diagnostic performance and inter-observer concordance in lesion detection with the automated breast volume scanner (ABVS). BMC Med Imaging 13:36. doi:10.1186/1471-2342-13-36

    Article  PubMed  PubMed Central  Google Scholar 

  31. Xiao YM, Chen ZH, Zhou QC, Wang Z (2015) The efficacy of automated breast volume scanning over conventional ultrasonography among patients with breast lesions. Int J Gynaecol Obstet 131:293–296

    Article  PubMed  Google Scholar 

  32. Giger ML, Inciardi MF, Edwards A et al (2016) Automated breast ultrasound in breast cancer screening of women with dense breasts: reader study of mammography-negative and mammography-positive cancers. AJR Am J Roentgenol 206:1341–1350

    Article  PubMed  Google Scholar 

  33. Leconte I, Feger C, Galant C et al (2003) Mammography and subsequent whole-breast sonography of nonpalpable breast cancers: the importance of radiologic breast density. AJR Am J Roentgenol 180:1675–1679

    Article  PubMed  Google Scholar 

  34. Berg WA, Zhang Z, Lehrer D et al (2012) Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA 307:1394–1404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Schaefer FK, Waldmann A, Katalinic A et al (2010) Influence of additional breast ultrasound on cancer detection in a cohort study for quality assurance in breast diagnosis—analysis of 102,577 diagnostic procedures. Eur Radiol 20:1085–1092

    Article  PubMed  Google Scholar 

  36. Berg WA, Bandos AI, Mendelson EB, Lehrer D, Jong RA, Pisano ED (2016) Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666. J Natl Cancer Inst 108:djv367

    Article  PubMed  Google Scholar 

  37. Ohuchi N, Suzuki A, Sobue T et al (2016) 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 387:341–348

    Article  PubMed  Google Scholar 

  38. Arleo EK, Saleh M, Ionescu D, Drotman M, Min RJ, Hentel K (2014) Recall rate of screening ultrasound with automated breast volumetric scanning (ABVS) in women with dense breasts: a first quarter experience. Clin Imaging 38:439–444

    Article  PubMed  Google Scholar 

  39. Wang ZL, Xu JH, Li JL, Huang Y, Tang J (2012) Comparison of automated breast volume scanning to hand-held ultrasound and mammography. Radiol Med 117:1287–1293

    Article  PubMed  Google Scholar 

  40. 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  PubMed  Google Scholar 

  41. Kim SH, Kang BJ, Choi BG et al (2013) Radiologists’ performance for detecting lesions and the interobserver variability of automated whole breast ultrasound. Korean J Radiol 14:154–163

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zhang Q, Hu B, Hu B, Li WB (2012) Detection of breast lesions using an automated breast volume scanner system. J Int Med Res 40:300–306

    Article  CAS  PubMed  Google Scholar 

  43. Golatta M, Franz D, Harcos A et al (2013) Interobserver reliability of automated breast volume scanner (ABVS) interpretation and agreement of ABVS findings with hand held breast ultrasound (HHUS), mammography and pathology results. Eur J Radiol 82:e332–e336

    Article  PubMed  Google Scholar 

  44. Golatta M, Baggs C, Schweitzer-Martin M et al (2015) Evaluation of an automated breast 3D-ultrasound system by comparing it with hand-held ultrasound (HHUS) and mammography. Arch Gynecol Obstet 291:889–895

    Article  PubMed  Google Scholar 

  45. 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  PubMed  Google Scholar 

  46. Kim H, Cha JH, Oh HY, Kim HH, Shin HJ, Chae EY (2014) Comparison of conventional and automated breast volume ultrasound in the description and characterization of solid breast masses based on BI-RADS features. Breast Cancer 21:423–428

    Article  PubMed  Google Scholar 

  47. Wenkel E, Heckmann M, Heinrich M et al (2008) Automated breast ultrasound: lesion detection and BI-RADS classification—a pilot study. Rofo 180:804–808

    Article  CAS  PubMed  Google Scholar 

  48. Zheng FY, Yan LX, Huang BJ et al (2015) Comparison of retraction phenomenon and BI-RADS-US descriptors in differentiating benign and malignant breast masses using an automated breast volume scanner. Eur J Radiol 84:2123–2129

    Article  PubMed  Google Scholar 

  49. Schmachtenberg C, Fischer T, Hamm B, Bick U (2017) Diagnostic performance of automated breast volume scanning (ABVS) compared to handheld ultrasonography with breast MRI as the gold standard. Acad Radiol 24:954–961

    Article  PubMed  Google Scholar 

  50. Meng Z, Chen C, Zhu Y et al (2015) Diagnostic performance of the automated breast volume scanner: a systematic review of inter-rater reliability/agreement and meta-analysis of diagnostic accuracy for differentiating benign and malignant breast lesions. Eur Radiol 25:3638–3647

    Article  PubMed  Google Scholar 

  51. Zhang J, Lai XJ, Zhu QL et al (2012) Interobserver agreement for sonograms of breast lesions obtained by an automated breast volume scanner. Eur J Radiol 81:2179–2183

    Article  PubMed  Google Scholar 

  52. Huang A, Zhu L, Tan Y et al (2016) Evaluation of automated breast volume scanner for breast conservation surgery in ductal carcinoma in situ. Oncol Lett 12:2481–2486

    PubMed  PubMed Central  Google Scholar 

  53. Wang X, Huo L, He Y et al (2016) Early prediction of pathological outcomes to neoadjuvant chemotherapy in breast cancer patients using automated breast ultrasound. Chin J Cancer Res 28:478–485

    Article  PubMed  PubMed Central  Google Scholar 

  54. Chae EY, Shin HJ, Kim HJ et al (2013) Diagnostic performance of automated breast ultrasound as a replacement for a hand-held second-look ultrasound for breast lesions detected initially on magnetic resonance imaging. Ultrasound Med Biol 39:2246–2254

    Article  PubMed  Google Scholar 

  55. Kim Y, Kang BJ, Kim SH, Lee EJ (2016) Prospective study comparing two second-look ultrasound techniques: handheld ultrasound and an automated breast volume scanner. J Ultrasound Med 35:2103–2112

    Article  PubMed  Google Scholar 

  56. Zheng FY, Lu Q, Huang BJ et al (2017) Imaging features of automated breast volume scanner: correlation with molecular subtypes of breast cancer. Eur J Radiol 86:267–275

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Medical Area, Institute of Diagnostic Radiology, University of Udine, Azienda Sanitaria Universitaria ‘S. Maria della Misericordia’, P.le Santa Maria della Misericordia, 15, 33100, Udine, Italy

    Martina Zanotel, Iliana Bednarova, Viviana Londero, Anna Linda, Michele Lorenzon, Rossano Girometti & Chiara Zuiani

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  1. Martina Zanotel
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  2. Iliana Bednarova
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  3. Viviana Londero
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  4. Anna Linda
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  5. Michele Lorenzon
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  6. Rossano Girometti
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  7. Chiara Zuiani
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Correspondence to Martina Zanotel.

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The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

The images of automated breast volumetric scanner (ABVS) and MRI shown in the figures were retrospectively selected among examinations previously performed according to an Ethical Committee approved trial investigating the role for ABVS in clinical practice, with informed consent obtained from all individual participants.

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Zanotel, M., Bednarova, I., Londero, V. et al. Automated breast ultrasound: basic principles and emerging clinical applications. Radiol med 123, 1–12 (2018). https://doi.org/10.1007/s11547-017-0805-z

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  • DOI: https://doi.org/10.1007/s11547-017-0805-z

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