Simple rules for ultrasonographic subcategorization of BI-RADS®-US 4 breast masses

医学 双雷达 子类别化 超声科 放射科 简单(哲学) 乳房成像 无线电技术 乳腺摄影术 人工智能 乳腺癌 内科学 癌症 哲学 计算机科学 动词 认识论
作者
Rodrigo Menezes Jales,Luı́s Otávio Sarian,Renato Zocchio Torresan,Emílio Francisco Marussi,Beatriz Regina Álvares,Sophie Derchain
出处
期刊:European Journal of Radiology [Elsevier]
卷期号:82 (8): 1231-1235 被引量:27
标识
DOI:10.1016/j.ejrad.2013.02.032
摘要

To evaluate an objective method for ultrasonographic (US) subcategorization of BI-RADS(®)-US 4 breast masses based on clear and simple rules in order for woman to benefit from a more complete and homogeneous breast mass analysis.In this cross-sectional study, we selected 330 women, with 339 US breast masses, classified as BI-RADS(®)-US 4. Three physicians experienced in breast imaging independently reviewed all US images, assessing mass shape, margins, orientation, echo texture and vascularity. These experts further subdivided the masses into subcategories 4a, 4b and 4c, according to simple US rules. Inter-observer agreement was calculated for US features categories and for final subcategory assessment. We also estimated the positive predictive value (PPV) for BI-RADS(®)-US subcategories 4a, 4b and 4c assigned by each of the three observers.Pathological examination of all masses confirmed 144 (42%) malignant and 195 (58%) benign tumors. Moderate agreement was obtained for mass shape, margins, vascularity and for final BI-RADS(®)-US 4 subcategory. Substantial agreement was obtained for the description of mass orientation and echo texture. The PPV for subcategories 4a, 4b and 4c were, 17%, 45% and 85%, respectively, for the first observer and 20%, 38% and 79% and 17%, 40% and 85% for the other two observers.Standardization of a US subcategorization of BI-RADS(®)-US 4 breast masses seems to be feasible, with substantial inter-observer agreement and progressive increase in the PPV in the subcategories 4a, 4b and 4c, provided that clear and simple classification rules are defined.

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
zhengzehong完成签到,获得积分10
刚刚
刚刚
稻草人完成签到 ,获得积分10
2秒前
zho发布了新的文献求助30
3秒前
3秒前
cc只会嘻嘻完成签到 ,获得积分10
3秒前
zink驳回了ding应助
3秒前
习习发布了新的文献求助10
3秒前
经法发布了新的文献求助10
4秒前
4秒前
4秒前
tong完成签到,获得积分10
4秒前
L~完成签到,获得积分10
4秒前
kyokukou完成签到,获得积分10
4秒前
xiaofeiyu完成签到,获得积分10
4秒前
大力曲奇完成签到,获得积分10
5秒前
乐乐应助崔梦楠采纳,获得10
5秒前
5秒前
5秒前
无奈梦岚完成签到,获得积分10
5秒前
yug发布了新的文献求助10
5秒前
蒋时晏完成签到,获得积分0
6秒前
JamesPei应助zz采纳,获得10
6秒前
MADKAI发布了新的文献求助10
6秒前
6秒前
脑洞疼应助Leexxxhaoo采纳,获得10
7秒前
7秒前
7秒前
RC_Wang应助东东采纳,获得10
7秒前
大脸妹发布了新的文献求助10
8秒前
两张发布了新的文献求助10
9秒前
9秒前
Akim应助执着的小蘑菇采纳,获得10
9秒前
调研昵称发布了新的文献求助10
9秒前
念念发布了新的文献求助10
10秒前
畅快的鱼发布了新的文献求助10
10秒前
搞怪藏今完成签到 ,获得积分10
11秒前
yu发布了新的文献求助10
11秒前
11秒前
qifa发布了新的文献求助10
11秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527469
求助须知:如何正确求助?哪些是违规求助? 3107497
关于积分的说明 9285892
捐赠科研通 2805298
什么是DOI,文献DOI怎么找? 1539865
邀请新用户注册赠送积分活动 716714
科研通“疑难数据库(出版商)”最低求助积分说明 709678