Scientific Online Resource System

Varna Medical Forum

Automated Breast Ultrasound (Abus) – A Contemporary Overview

Georgi Valchev, Stanislava Mavrodinova, Mariana Yordanova, Nedyalko Lechev, Silvia Stoykova-Cherneva, Monika Zhekova


Breast cancer is a disease of exceptional social significance. Because of its high incidence and its potential for radical treatment in the early stages, a substantial amount of resources is being dedicated yearly to improve early detection. In recent years Western countries have begun implementing automated breast ultrasound (ABUS) machines – a modification of the standard manual ultrasonography device, aimed at creating a standardized, reproducible examination for screening and diagnosis, which would also allow for characterization of radiographically dense breasts – simultaneously a risk contingent for mammary cancer and a diagnostic challenge for standard x-ray mammography. Based on mechanical sound waves and the piezoelectric effect, the method is devoid of ionizing radiation. ABUS uses a transducer that automatically moves along an applicator, which is made to conform to the shape of the breast – scanning it in several planes. The images are processed by a computer, similarly to x-ray computed tomography, allowing for multiplanar analysis of each potential lesion. Currently ABUS is at its initial stages of development in Bulgaria – one of the very few hospitals to implement it successfully is St. Marina University Hospital in Varna, having successfully incorporated it into its diagnostic and screening algorithm.


radiology, roentgenology, ultrasound, breast cancer, diagnostic imaging, screening

Full Text


Benson SR, Blue J, Judd K, Harman JE (2004). Ultrasound is now better than mammography for the detection of invasive breast cancer. Am J Surg, 188, 381-5.

Berg WA, Gutierrez L, NessAiver MS, et al (2004). Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. Radiology, 233, 830-49.

Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med.2007;356:227-36.

Cancer Research UK, , Accessed August 2018.

Chang JM, Moon W.K., Cho N., Park J. S., Kim S.J., Breast cancers initially detected by hand-held ultrasound: detection performance of radiologists using automated breast ultrasound data, Sage Journals, Volume: 52 issue: 1, page(s): 8-14, First Published February 1, 2011,

Choi WJ, Cha JH, Kim HH, Shin HJ, Kim H, Chae EY, Hong MJ, Comparison of Automated Breast Volume Scanning and HandHeld Ultrasound in the Detection of Breast Cancer: an Analysis of 5,566 Patient Evaluations, Asian Pac J Cancer Prev, 15 (21), 9101-9105, DOI:

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

Inciardi MF, Automated breast ultrasound: An update. Appl Radiol., September 05, 2014, available online at , accessed on 20 August 2018.

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

Kaplan SS. Automated whole breast ultrasound. Radiol Clin North Am. 2014;52:539–546.

Kelly KM, Dean J, Comulada WS et al., Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts, European Radioliology, March 2010, Volume 20, Issue 3, pp 734–742. , Springer-Verlag, Print ISSN 0938-7994, Online ISSN 1432-1084

Kelly KM. Dean J, Lee SJ, Comulada W.S., Breast cancer detection: radiologists’ performance using mammography with and without automated whole-breast ultrasound, European Radiology, November 2010, Volume 20, Issue 11, pp 2557–2564

Kuhl CK, Schrading S, Leutner CC, Morakkabati-Spitz N, Wardelmann E, Fimmers R, Kuhn W, Schild HH, Mammography, Breast Ultrasound, and Magnetic Resonance Imaging for Surveillance of Women at High Familial Risk for Breast Cancer, Journal Of Clinical Oncology, Volume 23, Number 33, November 20 2005

Li N, Jiang YX, Zhu QL, Zhang J, Dai Q, Liu H, et al. 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. 2013;39:2255–2263.

Maturo VG, Zusmer NR, Gilson AJ, Smoak WM, Janowitz WR, Bear BE, et al. Ultrasound of the whole breast utilizing a dedicated automated breast scanner. Radiology. 1980;137:457–463.

Shin HJ, Kim HH, Cha JH, Current status of automated breast ultrasonography, Ultrasonography. 2015 Jul; 34(3): 165–172, Published online 2015 Mar 23. doi: 10.14366/usg.15002 ,PMCID: PMC4484287, PMID: 25971900

Tabar L, Vitak B, Chen TH, Yen AM, Cohen A, Tot T, et al. Swedish two-county trial: impact of mammographic screening on breast cancer mortality during 3 decades. Radiology. 2011;260:658–663.

Tabar L, Dean PB. Mammographic parenchymal patterns: risk indicator for breast cancer? JAMA. 1982;247:185-189.

Tan T, Platel B, Twellmann T, van Schie G, Mus R, Grivegnee A, et al. Evaluation of the effect of computer-aided classification of benign and malignant lesions on reader performance in automated three-dimensional breast ultrasound. Acad Radiol. 2013;20:1381–1388.

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). Available from:

van Zelsta JCM, Tan T, Platel B, Jong M, Steenbakkers A, Mourits M, Grivegnee A, Borelli C, Karssemeijer N, Manna RM Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection, European Journal of Radiology, Volume 89, April 2017, Pages 54-59,

Wang FL, Chen F, Yin H, et al (2013). Effects of age, breast density and volume on breast cancer diagnosis: a retrospective comparison of sensitivity of mammography and ultrasonography in China's rural areas. Asian Pac J Cancer Prev, 14, 2277-82.

Wenkel E, Heckmann M, Heinrich M, Schwab SA, Uder M, Schulz-Wendtland R, et al. Automated breast ultrasound: lesion detection and BI-RADS classification--a pilot study. Rofo. 2008;180:804–808.

Wilczek B, Wilczek HE, Rasouliyan L, Leifland K, 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, European Journal of Radiology, Volume 85, Issue 9, September 2016, Pages 1554-1563,

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

Wojcinski S, Farrokh A, Hille U, Wiskirchen J, Gyapong S, Soliman A, Degenhardt F, Hillemanns P, The Automated Breast Volume Scanner (ABVS): initial experiences in lesion detection compared with conventional handheld B-mode ultrasound: a pilot study of 50 cases, Int J Womens Health. 2011; 3: 337–346. Published online 2011 Oct 11. doi: 10.2147/IJWH.S23918, PMCID: PMC3221417, PMID: 22114526

Wolfe JN. Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol.1976;126:1130-1137.

Wolfe JN. Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer.1976;37:2486-2492.



Font Size