Automated Breast Ultrasound for Breast Lesion Detection - Literature Review

Authors

  • Elena Ivanova Medical University “Prof. Dr. Paraskev Stoyanov” – Varna
  • Boyko Matev Medical University “Prof. Dr. Paraskev Stoyanov” – Varna
  • Yanka Baneva Medical University “Prof. Dr. Paraskev Stoyanov” – Varna

DOI:

https://doi.org/10.46328/ijonest.5890

Keywords:

ABUS, Breast ultrasound, AI in radiology, Breast cancer, Cancer screening

Abstract

Early breast lesion detection is crucial for women. According to WHO, breast cancer caused 670 000 deaths globally in 2022. Mammography is one of the best screening tools for early breast cancer detection. Still, with a dose between 3 and 5 mGy to the glandular tissue for a typical mammographic screening examination involving two views of each breast, alternatives might be used if not necessary. Breast ultrasound is an option, especially in young patients and those with dense breasts. This paper aims to present the little-known automated breast ultrasound (ABUS) as a viable alternative to the conventional handheld ultrasound. A literature review was conducted using the keywords “automated breast ultrasound," "ABUS," "ABVUS," and "sonotomography" in the online databases Google Scholar, PubMed, and Web of Science. The review yielded a total of n=267 studies, of which n=83 were considered pertinent. The review was also based on the clinical experience using an ABUS machine at St. Marina University Hospital—Varna (UMHAT “St. Marina Varna”). ABUS is not widely used in most radiology clinics, even dedicated breast clinics. Automated ultrasound is a machine controlled by an X-ray technician or an ultrasonographer, which records a series of breast ultrasound images. The main advantage of the modality is the elimination of operator dependence (with some caveats, which are expanded upon in the “discussion section”), as well as the reduction of subjectivity. If previous studies are available, precise follow-up of the size and localization of breast lesions is possible. However, а suitable additional screening method, АBUS, demonstrates some disadvantages as a diagnostic tool. Some of the limitations are the absence of information for vascularization and the elasticity of a lesion. Possible artefacts include, but are not limited to, retro-areolar shadowing, breathing artefacts, and air interposition between the probe and the skin. ABUS is applicable for screening and follow-up modality, especially in settings with a large patient base or an absence of trained personnel.

References

Al Jahed, D., Dekeyzer, S., Vanwambeke, K., Antic, M., Vanhoenacker, C., & Vanhoenacker, F. (2022). Automated breast ultrasound (ABUS): A pictorial essay of common artifacts and benign and malignant pathology. Journal of Ultrasonography, 22(91), e222. https://doi.org/10.15557/JoU.2022.0037

Boca, I. B., Ciurea, A. I., Ciortea, C. A., & Dudea, S. M. (2021). Pros and cons for automated breast ultrasound (ABUS): A narrative review. Journal of Personalized Medicine, 11(6), 512. https://doi.org/10.3390/jpm11060512

Brem, R. F., Tabár, L., Duffy, S. W., Inciardi, M. F., Guingrich, J. A., Hashimoto, B. E., 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(3), 663–673. https://doi.org/10.1148/radiol.14132832

Chang, J. M., Cha, J. H., Park, J. S., Kim, S. J., & Moon, W. K. (2015). Automated breast ultrasound system (ABUS): Reproducibility of mass localization, size measurement, and characterization on serial examinations. Acta Radiologica, 56(10), 1163–1170. https://doi.org/10.1177/0284185114551565

Dempsey, P. J. (2004). The history of breast ultrasound. Journal of Ultrasound in Medicine, 23(7), 887–894.

Foglia, E., Marinelli, S., Garagiola, E., Ferrario, L., Depretto, C., Cartia, F., et al. (2020). Budget impact analysis of breast cancer screening in Italy: The role of new technologies. Health Services Management Research, 33(2), 66–75. https://doi.org/10.1177/0951484819883401

Goldberg, B. B. (1988). Medical diagnostic ultrasound: A retrospective on its 40th anniversary. Kodak Health Sciences.

Hatzipanagiotou, M. E., Huber, D., Gerthofer, V., Hetterich, M., Ripoll, B. R., Ortmann, O., et al. (2022). Feasibility of ABUS as an alternative to handheld ultrasound for response control in neoadjuvant breast cancer treatment. Clinical Breast Cancer, 22(2), e142–e146. https://doi.org/10.1016/j.clbc.2021.07.003

Hooley, R. J., Greenberg, K. L., Stackhouse, R. M., Geisel, J. L., Butler, R. S., & Philpotts, L. E. (2012). Screening US in patients with mammographically dense breasts. Radiology, 265(1), 59–69. https://doi.org/10.1148/radiol.12120621

Huppe, A. I., Inciardi, M. F., Redick, M., Carroll, M., Buckley, J., Hill, J. D., et al. (2018). Automated breast ultrasound interpretation times. Academic Radiology, 25(12), 1577–1581. https://doi.org/10.1016/j.acra.2018.03.021

Icanervilia, A. V., Poelhekken, K., Thobari, J. A., Choridah, L., Hutajulu, S. H., de Bock, G. H., et al. (2025). Cost-effectiveness analysis of mammography-based breast cancer screening in Indonesia. Value in Health Regional Issues, 48, 101112. https://doi.org/10.1016/j.vhri.2024.101112

Jiang, Y., Inciardi, M. F., Edwards, A. V., & Papaioannou, J. (2018). Interpretation time using CAD for automated breast ultrasound. American Journal of Roentgenology, 211(2), 452–461. https://doi.org/10.2214/AJR.18.19516

Lee, J. M., Partridge, S. C., Liao, G. J., Hippe, D. S., Kim, A. E., Lee, C. I., et al. (2019). Double reading of automated breast ultrasound. Clinical Imaging, 55, 119–125. https://doi.org/10.1016/j.clinimag.2019.01.006

Lee, J., Kang, B. J., Kim, S. H., & Park, G. E. (2022). Evaluation of computer-aided detection in screening automated breast ultrasound. Diagnostics, 12(5), 1123. https://doi.org/10.3390/diagnostics12051123

Marcon, M., Fuchsjäger, M. H., Clauser, P., & Mann, R. M. (2024). ESR essentials: Screening for breast cancer. European Radiology, 34(10), 6348–6357. https://doi.org/10.1007/s00330-024-10740-5

Vourtsis, A. (2019). Three-dimensional automated breast ultrasound: Technical aspects and first results. Diagnostic and Interventional Imaging, 100(10), 579–592. https://doi.org/10.1016/j.diii.2019.07.002

Wilczek, B., Wilczek, H. E., Rasouliyan, L., & Leifland, K. (2016). Adding 3D automated breast ultrasound to mammography screening. European Journal of Radiology, 85(9), 1554–1563. https://doi.org/10.1016/j.ejrad.2016.06.020

Winkelman, A. J., Tulenko, K., Epstein, S. H., Nguyen, J. V., Ford, C., & Miller, M. M. (2024). Breast cancer screening with automated breast ultrasound. Journal of Breast Imaging, 6(5), 493–501. https://doi.org/10.1093/jbi/wbad066

Xu, Y., Xu, Y., Shen, S., Mao, F., Zhang, X., Zhang, Y., et al. (2024). Diagnostic efficacy of automatic breast volume scanner ultrasound. Frontiers in Oncology, 14, 1421425. https://doi.org/10.3389/fonc.2024.1421425

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Published

2025-12-31

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Section

Science

How to Cite

Automated Breast Ultrasound for Breast Lesion Detection - Literature Review . (2025). International Journal on Engineering, Science and Technology, 7(2), 107-117. https://doi.org/10.46328/ijonest.5890