Application of a shear-wave elastography prediction model to distinguish between benign and malignant breast lesions and the adjustment of ultrasound Breast Imaging Reporting and Data System classifications

医学 超声波 放射科 弹性成像 乳腺超声检查 乳房成像 双雷达 逻辑回归 乳腺癌 鉴别诊断 超声弹性成像 病理 乳腺摄影术 癌症 内科学
作者
Yanyan Yu,X. Ye,Jun Yang,L. Chen,M. Zhang,Yong He,Z. Chen
出处
期刊:Clinical Radiology [Elsevier BV]
卷期号:77 (2): e147-e153 被引量:5
标识
DOI:10.1016/j.crad.2021.10.016
摘要

To explore a real-time shear-wave elastography (SWE) prediction model distinguishing benign from malignant breast lesions and to determine its application in adjusting ultrasound Breast Imaging Reporting and Data System (BI-RADS) classifications.Four hundred and sixty-eight patients with 488 breast lesions were enrolled. Patients underwent hollow-needle puncture or surgical resection for histopathological examinations. Ultrasound examinations, both conventional ultrasound and real-time SWE, were performed <2 weeks prior to sampling. Statistical analyses were implemented to distinguish benign from malignant breast lesions and adjust ultrasound BI-RADS 3 and 4a classifications.The real-time SWE indicators Emax and Ecol showed the highest diagnostic efficiency in distinguishing between benign and malignant lesions through quantitative and qualitative indicators, respectively. The area under the curve (AUC) for Emax was 0.837 while that for Ecol was 0.828. The AUC of the real-time SWE prediction model, constructed by multivariate logistic regression, for diagnosing benign and malignant breast lesions was 0.850.The real-time SWE prediction model aids in the differential diagnosis of benign and malignant breast lesions but cannot replace conventional ultrasound. The model improves the diagnostic performance of ultrasound BI-RADS 3 and 4a classifications.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
在水一方应助Lucifer采纳,获得10
1秒前
思源应助sci大户采纳,获得10
2秒前
潘特完成签到,获得积分10
2秒前
ZEM发布了新的文献求助10
4秒前
sht发布了新的文献求助10
5秒前
gzgljh完成签到,获得积分0
7秒前
盘菜应助gugugaga采纳,获得10
7秒前
赘婿应助aaa采纳,获得10
8秒前
zhangnan完成签到,获得积分10
8秒前
安颜演发布了新的文献求助10
8秒前
闾丘晓蓝完成签到 ,获得积分10
9秒前
10秒前
10秒前
开开运气爆棚完成签到 ,获得积分10
11秒前
坤坤发布了新的文献求助10
11秒前
11秒前
11秒前
11秒前
沉默凡桃完成签到,获得积分20
15秒前
乐乐应助碎觉觉采纳,获得10
15秒前
nefu biology发布了新的文献求助10
15秒前
sci大户发布了新的文献求助10
16秒前
鲤鱼紫寒完成签到,获得积分10
16秒前
月光颂博客完成签到 ,获得积分10
16秒前
16秒前
16秒前
Ava应助麻辣香锅采纳,获得10
16秒前
BLUZ完成签到,获得积分10
17秒前
17秒前
天天快乐应助知来者采纳,获得10
18秒前
沉默凡桃发布了新的文献求助10
19秒前
科研通AI6.2应助ayu采纳,获得10
20秒前
传统的裘完成签到,获得积分10
20秒前
初景发布了新的文献求助10
21秒前
生动书竹发布了新的文献求助10
22秒前
22秒前
jimaohi发布了新的文献求助10
23秒前
23秒前
23秒前
24秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 1500
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
Advanced Weaponeering Fourth Edition, Volume 2 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7508504
求助须知:如何正确求助?哪些是违规求助? 9097255
关于积分的说明 19413729
捐赠科研通 7115633
什么是DOI,文献DOI怎么找? 3252223
关于科研通互助平台的介绍 2421368
邀请新用户注册赠送积分活动 2238563