Literature DB >> 30962169

Three-dimensional automated breast ultrasound: Technical aspects and first results.

A Vourtsis1.   

Abstract

Three-dimensional automated breast ultrasound system (3D ABUS) is an innovation in breast ultrasound that has been developed to uncouple detection from image acquisition and to address the limitations of handheld ultrasound (HHUS). 3D ABUS provides a large field of view using high frequency transducers, producing high-resolution images and covering a large portion of the breast with one sweep. As more data become available on breast density and the impact of supplemental screening, 3D ABUS has gained wider acceptance as an adjunct tool to mammography. Computer-aided detection software significantly reduces interpretation time, improving the workflow for the utilization of 3D ABUS as a supplemental screening tool. In the diagnostic setting, 3D ABUS offers valuable impact in the detectability of breast lesions and the differentiation of malignant from benign lesions, with a high inter-observer agreement. State-of-the art technique, including uniform compression and proper positioning, tends to reduce artifactual posterior shadowing, while combined 3D ABUS-mammography interpretation improves radiologists' diagnostic performance. Promising results have supported the enhanced efficiency of 3D ABUS in detecting the extent of breast cancer and assessing response to neoadjuvant chemotherapy, whereas its correlation with molecular subtypes of breast cancer is remarkable. Future perspectives include the integration of radiomics and deep learning in the further development of 3D ABUS.
Copyright © 2019 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Entities:  

Keywords:  3D automated breast ultrasound; Breast cancer; Dense breasts; Handheld ultrasound; Inter-observer agreement

Mesh:

Year:  2019        PMID: 30962169     DOI: 10.1016/j.diii.2019.03.012

Source DB:  PubMed          Journal:  Diagn Interv Imaging        ISSN: 2211-5684            Impact factor:   4.026


  8 in total

Review 1.  Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer.

Authors:  Xianshu Kong; Qian Zhang; Xuemei Wu; Tianning Zou; Jiajun Duan; Shujie Song; Jianyun Nie; Chu Tao; Mi Tang; Maohua Wang; Jieya Zou; Yu Xie; Zhenhui Li; Zhen Li
Journal:  Front Oncol       Date:  2022-05-20       Impact factor: 5.738

Review 2.  What scans we will read: imaging instrumentation trends in clinical oncology.

Authors:  Thomas Beyer; Luc Bidaut; John Dickson; Marc Kachelriess; Fabian Kiessling; Rainer Leitgeb; Jingfei Ma; Lalith Kumar Shiyam Sundar; Benjamin Theek; Osama Mawlawi
Journal:  Cancer Imaging       Date:  2020-06-09       Impact factor: 3.909

3.  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

4.  The effect of vaginal delivery and Caesarean section on the anal Sphincter complex of Primipara based on optimized three-dimensional ultrasound image and nuclear regression Reconstruction Algorithm.

Authors:  Naxin He; Liang Shi
Journal:  Pak J Med Sci       Date:  2021       Impact factor: 1.088

5.  A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.

Authors:  Jignesh Chowdary; Pratheepan Yogarajah; Priyanka Chaurasia; Velmathi Guruviah
Journal:  Ultrason Imaging       Date:  2022-02-07       Impact factor: 1.578

6.  Predicting the molecular subtypes of breast cancer using nomograms based on three-dimensional ultrasonography characteristics.

Authors:  Xiaojing Xu; Liren Lu; Luoxi Zhu; Yanjuan Tan; Lifang Yu; Lingyun Bao
Journal:  Front Oncol       Date:  2022-08-19       Impact factor: 5.738

7.  Evaluation of a new method of calculating breast tumor volume based on automated breast ultrasound.

Authors:  Jing-Jing Ma; Shan Meng; Sha-Jie Dang; Jia-Zhong Wang; Quan Yuan; Qi Yang; Can-Xu Song
Journal:  Front Oncol       Date:  2022-09-13       Impact factor: 5.738

8.  Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer.

Authors:  Hongxu Zhang; Haiwang Liu; Lihui Ma; Jianping Liu; Dawei Hu
Journal:  J Healthc Eng       Date:  2021-09-17       Impact factor: 2.682

  8 in total

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