Literature DB >> 31221510

Reducing Unnecessary Biopsy of Breast Lesions: Preliminary Results with Combination of Strain and Shear-Wave Elastography.

Jing Han1, Fei Li1, Chuan Peng1, Yini Huang1, Qingguang Lin1, Yubo Liu1, Longhui Cao2, Jianhua Zhou3.   

Abstract

The aim of our study was to compare strain elastography (SE), acoustic radiation force impulse-inducing Virtual Touch Imaging ([VTI] Siemens Medical Solutions, Mountain View, CA, USA), Virtual Touch Imaging Quantification ([VTIQ] Siemens Medical Solutions) and combined methods in the evaluation of ultrasound (US) Breast Imaging-Reporting and Data System (BI-RADS) category 4 lesions to explore an applicable way to reduce unnecessary biopsy by reducing false positives of conventional US without yielding false-negative cases. A total of 267 patients with 278 BI-RADS category 4 lesions (151 benign and 127 malignant) were evaluated with conventional B-mode US, SE, VTI and VTIQ implemented on a Siemens Acuson S2000 US system. Diagnostic performance, including area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were evaluated. Overall, VTI alone exhibited the highest NPV (91.74%), although combined elastic methods exhibited higher NPV than single methods, with the highest NPV at 100% when the VTI, SE and VTIQ methods were combined. Compared with conventional US, PPV increased from 45.7% (127 of 278) to 63.18% (127 of 201) when adding combined elastography (VTI + SE +VTIQ). In addition, 52.5% (63/120) and 50.8% (61/120) of BI-RADS 4 A lesions were downgraded when using combined methods (VTI + SE and VTI + SE + VTIQ, respectively) without missing any cancer. However, 2 intraductal papillomas and 1 phyllodes tumor were not identified. In conclusion, the combination of different elastic methods have the potential to downgrade BI-RADS 4A lesions to reduce false-positive biopsies without increasing the risk of missing cancers.
Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Acoustic radiation force impulse (ARFI); Biopsy; Breast Imaging-Reporting and Data System; Breast Ultrasound; Breast lesion; Combination; Comparison; Diagnostic performance; Shear-wave elastography; Strain elastography

Mesh:

Year:  2019        PMID: 31221510     DOI: 10.1016/j.ultrasmedbio.2019.05.014

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  5 in total

1.  Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head.

Authors:  Chiagoziem C Ukwuoma; Md Altab Hossain; Jehoiada K Jackson; Grace U Nneji; Happy N Monday; Zhiguang Qin
Journal:  Diagnostics (Basel)       Date:  2022-05-05

2.  Comparing the accuracy between shear wave elastography and strain elastography in the diagnosis of breast tumors: Systematic review and meta-analysis.

Authors:  Huayu Wu; Shengnan Zhang; Cong Wang; Yumei Yan
Journal:  Medicine (Baltimore)       Date:  2022-05-06       Impact factor: 1.817

3.  Evaluation of internal and shell stiffness in the differential diagnosis of breast non-mass lesions by shear wave elastography.

Authors:  Ping Xu; Mei Wu; Min Yang; Juan Xiao; Zheng-Min Ruan; Lan-Ying Wu
Journal:  World J Clin Cases       Date:  2020-06-26       Impact factor: 1.337

4.  Which combination of different ultrasonography modalities is more appropriate to diagnose breast cancer?: A network meta-analysis (a PRISMA-compliant article).

Authors:  Yang Zhou; Jialing Wu
Journal:  Medicine (Baltimore)       Date:  2022-08-05       Impact factor: 1.817

5.  Establishment of a deep-learning system to diagnose BI-RADS4a or higher using breast ultrasound for clinical application.

Authors:  Tetsu Hayashida; Erina Odani; Masayuki Kikuchi; Aiko Nagayama; Tomoko Seki; Maiko Takahashi; Noriyuki Futatsugi; Akiko Matsumoto; Takeshi Murata; Rurina Watanuki; Takamichi Yokoe; Ayako Nakashoji; Hinako Maeda; Tatsuya Onishi; Sota Asaga; Takashi Hojo; Hiromitsu Jinno; Keiichi Sotome; Akira Matsui; Akihiko Suto; Shigeru Imoto; Yuko Kitagawa
Journal:  Cancer Sci       Date:  2022-08-03       Impact factor: 6.518

  5 in total

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