Literature DB >> 29624482

Automated Three-dimensional Breast US for Screening: Technique, Artifacts, and Lesion Characterization.

Jan C M van Zelst1, Ritse M Mann1.   

Abstract

Automated breast (AB) ultrasonography (US) scanners have recently been brought to market for breast imaging. AB US devices use mechanically driven wide linear-array transducers that can image whole-breast US volumes in three dimensions. AB US is proposed for screening as a supplemental modality to mammography in women with dense breasts and overcomes important limitations of whole-breast US using handheld devices, such as operator dependence and limited reproducibility. A literature review of supplemental whole-breast US for screening was performed, which showed that both AB US and handheld US allow detection of mammographically negative early-stage invasive breast cancers but also increase the false-positive recall rate. Technicians with limited training can perform AB US; nevertheless, there is a learning curve for acquiring optimal images. Proper acquisition technique may allow avoidance of common artifacts that could impair interpretation of AB US results. Regardless, interpretation of AB US results can be challenging. This article reviews the US appearance of common benign and malignant lesions and presents examples of false-positive and false-negative AB US results. In situ breast cancers are rarely detected with supplemental whole-breast US. The most discriminating feature that separates AB US from handheld US is the retraction phenomenon on coronal reformatted images. The retraction phenomenon is rarely seen with benign findings but accompanies almost all breast cancers. In conclusion, women with dense breasts may benefit from supplemental AB US examinations. Understanding the pitfalls in acquisition technique and lesion interpretation, both of which can lead to false-positive recalls, might reduce the potential harm of performing supplemental AB US. Online supplemental material is available for this article. ©RSNA, 2018.

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Year:  2018        PMID: 29624482     DOI: 10.1148/rg.2018170162

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  10 in total

1.  The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

Authors:  Xiao Luo PhD; Min Xu; Guoxue Tang; Yi Wang PhD; Na Wang; Dong Ni PhD; Xi Lin PhD; An-Hua Li
Journal:  Br J Radiol       Date:  2021-12-15       Impact factor: 3.039

2.  The Role of ABUS in The Diagnosis of Breast Cancer.

Authors:  Elżbieta Łuczyńska; Marta Pawlak; Tadeusz Popiela; Wojciech Rudnicki
Journal:  J Ultrason       Date:  2022-04-27

3.  Image quality and artifacts in automated breast ultrasonography.

Authors:  Sung Hun Kim
Journal:  Ultrasonography       Date:  2018-07-14

4.  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 5.  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

6.  False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients.

Authors:  Youngjune Kim; Jiwon Rim; Sun Mi Kim; Bo La Yun; So Yeon Park; Hye Shin Ahn; Bohyoung Kim; Mijung Jang
Journal:  Ultrasonography       Date:  2020-03-24

Review 7.  Evaluation of Diagnostic Performance of Automatic Breast Volume Scanner Compared to Handheld Ultrasound on Different Breast Lesions: A Systematic Review.

Authors:  Shahad A Ibraheem; Rozi Mahmud; Suraini Mohamad Saini; Hasyma Abu Hassan; Aysar Sabah Keiteb; Ahmed M Dirie
Journal:  Diagnostics (Basel)       Date:  2022-02-19

8.  Application of Convolution Neural Network Algorithm Based on Multicenter ABUS Images in Breast Lesion Detection.

Authors:  Jianxing Zhang; Xing Tao; Yanhui Jiang; Xiaoxi Wu; Dan Yan; Wen Xue; Shulian Zhuang; Ling Chen; Liangping Luo; Dong Ni
Journal:  Front Oncol       Date:  2022-07-04       Impact factor: 5.738

9.  Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions.

Authors:  Shi-Jie Wang; Hua-Qing Liu; Tao Yang; Ming-Quan Huang; Bo-Wen Zheng; Tao Wu; Chen Qiu; Lan-Qing Han; Jie Ren
Journal:  Diagnostics (Basel)       Date:  2022-01-12

10.  Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks.

Authors:  Jeongmin Lee; Bong Joo Kang; Sung Hun Kim; Ga Eun Park
Journal:  Diagnostics (Basel)       Date:  2022-02-24
  10 in total

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