Literature DB >> 29555568

Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use.

Rossella Rella1, Paolo Belli2, Michela Giuliani2, Enida Bufi2, Giorgio Carlino2, Pierluigi Rinaldi2, Riccardo Manfredi2.   

Abstract

Automated breast ultrasonography (ABUS) is a new imaging technology for automatic breast scanning through ultrasound. It was first developed to overcome the limitation of operator dependency and lack of standardization and reproducibility of handheld ultrasound. ABUS provides a three-dimensional representation of breast tissue and allows images reformatting in three planes, and the generated coronal plane has been suggested to improve diagnostic accuracy. This technique has been first used in the screening setting to improve breast cancer detection, especially in mammographically dense breasts. In recent years, numerous studies also evaluated its use in the diagnostic setting: they showed its suitability for breast cancer staging, evaluation of tumor response to neoadjuvant chemotherapy, and second-look ultrasound after magnetic resonance imaging. The purpose of this article is to provide a comprehensive review of the current body of literature about the clinical performance of ABUS, summarize available evidence, and identify gaps in knowledge for future research.
Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  3D scanning; Automated breast ultrasound; breast cancer; coronal view; ultrasonography

Mesh:

Year:  2018        PMID: 29555568     DOI: 10.1016/j.acra.2018.02.014

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  18 in total

1.  Automated breast volume scanner (ABVS) compared to handheld ultrasound (HHUS) and contrast-enhanced magnetic resonance imaging (CE-MRI) in the early assessment of breast cancer during neoadjuvant chemotherapy: an emerging role to monitoring tumor response?

Authors:  Anna D'Angelo; Armando Orlandi; Enida Bufi; Sara Mercogliano; Paolo Belli; Riccardo Manfredi
Journal:  Radiol Med       Date:  2021-01-01       Impact factor: 3.469

2.  What eye tracking can tell us about how radiologists use automated breast ultrasound.

Authors:  Jeremy M Wolfe; Wanyi Lyu; Jeffrey Dong; Chia-Chien Wu
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-26

3.  Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis.

Authors:  Jia-Wei Li; Yu-Cheng Cao; Zhi-Jin Zhao; Zhao-Ting Shi; Xiao-Qian Duan; Cai Chang; Jian-Gang Chen
Journal:  Eur Radiol       Date:  2021-09-14       Impact factor: 7.034

4.  Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

Authors:  Qiucheng Wang; He Chen; Gongning Luo; Bo Li; Haitao Shang; Hua Shao; Shanshan Sun; Zhongshuai Wang; Kuanquan Wang; Wen Cheng
Journal:  Eur Radiol       Date:  2022-04-30       Impact factor: 7.034

5.  Factors affecting the concordance of radiologic and pathologic tumor size in breast carcinoma.

Authors:  Ameer Hamza; Sidrah Khawar; Ramen Sakhi; Ahmed Alrajjal; Shelby Miller; Warda Ibrar; Jacob Edens; Sajad Salehi; Daniel Ockner
Journal:  Ultrasound       Date:  2018-10-23

6.  Automated Breast Ultrasound: Interobserver Agreement, Diagnostic Value, and Associated Clinical Factors of Coronal-Plane Image Features.

Authors:  Guoxue Tang; Xin An; Huiling Xiang; Lixian Liu; Anhua Li; Xi Lin
Journal:  Korean J Radiol       Date:  2020-05       Impact factor: 3.500

7.  Automated Breast Ultrasound System for Breast Cancer Evaluation: Diagnostic Performance of the Two-View Scan Technique in Women with Small Breasts.

Authors:  Bo Ra Kwon; Jung Min Chang; Soo Yeon Kim; Su Hyun Lee; Soo Yeon Kim; So Min Lee; Nariya Cho; Woo Kyung Moon
Journal:  Korean J Radiol       Date:  2020-01       Impact factor: 3.500

Review 8.  Automated Breast Ultrasound Screening for Dense Breasts.

Authors:  Sung Hun Kim; Hak Hee Kim; Woo Kyung Moon
Journal:  Korean J Radiol       Date:  2020-01       Impact factor: 3.500

9.  Influence of Breast Density on Patient's Compliance during Ultrasound Examination: Conventional Handheld Breast Ultrasound Compared to Automated Breast Ultrasound.

Authors:  Sara De Giorgis; Nicole Brunetti; Jeries Zawaideh; Federica Rossi; Massimo Calabrese; Alberto Stefano Tagliafico
Journal:  J Med Ultrasound       Date:  2020-06-04

10.  Breast ultrasound: automated or hand-held? Exploring patients' experience and preference.

Authors:  Ilaria Mussetto; Licia Gristina; Simone Schiaffino; Simona Tosto; Edoardo Raviola; Massimo Calabrese
Journal:  Eur Radiol Exp       Date:  2020-02-10
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