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

被引:55
作者
Vourtsis, A. [1 ]
机构
[1] Diagnost Mammog Med Diagnost Imaging Unit, Kifisias Ave 362, Athens 15233, Greece
关键词
3D automated breast ultrasound; Handheld ultrasound; Dense breasts; Breast cancer; Inter-observer agreement; HAND-HELD ULTRASOUND; VOLUME SCANNING ABVS; DENSE BREASTS; INTEROBSERVER AGREEMENT; DIAGNOSTIC PERFORMANCE; LESION DETECTION; DIFFERENTIATING BENIGN; SCREENING ULTRASOUND; 2ND-LOOK ULTRASOUND; INITIAL-EXPERIENCE;
D O I
10.1016/j.diii.2019.03.012
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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 neoadjuvantchemotherapy, 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. (C) 2019 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:579 / 592
页数:14
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