已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography

医学 恶性肿瘤 双雷达 乳房成像 放射科 乳腺超声检查 预测值 接收机工作特性 超声波 超声科 深度学习 队列 乳腺癌 人工智能 乳腺摄影术 病理 计算机科学 内科学 癌症
作者
Zhi‐jin Zhao,Size Hou,Shuang Li,Danli Sheng,Qi Liu,Cai Chang,Jiangang Chen,Jiawei Li
出处
期刊:Ultrasound in Medicine and Biology [Elsevier BV]
卷期号:48 (11): 2267-2275 被引量:5
标识
DOI:10.1016/j.ultrasmedbio.2022.06.019
摘要

The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the pre-operative US examination. A total of 479 breast lesions diagnosed as BI-RADS 4A in pre-operative US examination were enrolled. There were 362 benign lesions and 117 malignant lesions confirmed by postoperative pathology with a malignancy rate of 24.4%. US images were collected from the database server. They were then randomly divided into training and testing cohorts at a ratio of 4:1. To correctly classify malignant and benign tumors diagnosed as BI-RADS 4A in US, four deep learning models, including MobileNet, DenseNet121, Xception and Inception V3, were developed. The performance of deep learning models was compared using the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Meanwhile, the robustness of the models was evaluated by five-fold cross-validation. Among the four models, the MobileNet model turned to be the optimal model with the best performance in classifying benign and malignant lesions among BI-RADS 4A breast lesions. The AUROC, accuracy, sensitivity, specificity, PPV and NPV of the optimal model in the testing cohort were 0.897, 0.913, 0.926, 0.899, 0.958 and 0.784, respectively. About 14.4% of patients were expected to be upgraded to BI-RADS 4B in US with the assistance of the MobileNet model. The deep learning model MobileNet can help to reduce the rate of malignancy among BI-RADS 4A breast lesions in pre-operative US examinations, which is valuable to clinicians in tailoring treatment for suspicious breast lesions identified on US.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
linghu完成签到 ,获得积分10
2秒前
Tendency完成签到 ,获得积分10
4秒前
善学以致用应助皮皮蟹采纳,获得10
8秒前
8秒前
8秒前
12秒前
小小斌完成签到,获得积分10
12秒前
LMX发布了新的文献求助10
17秒前
20秒前
20秒前
yang完成签到 ,获得积分10
23秒前
沉静安荷给沉静安荷的求助进行了留言
23秒前
皮皮蟹发布了新的文献求助10
24秒前
轻松的惜芹应助科研达人采纳,获得10
26秒前
广州小肥羊完成签到 ,获得积分10
29秒前
皮皮蟹完成签到,获得积分10
33秒前
完美世界应助ceeray23采纳,获得20
38秒前
曾经的电脑完成签到 ,获得积分10
43秒前
Sky完成签到,获得积分10
43秒前
握瑾怀瑜完成签到 ,获得积分0
44秒前
轻松的惜芹应助科研达人采纳,获得10
47秒前
Orange应助高兴的忆曼采纳,获得10
49秒前
平淡访冬完成签到 ,获得积分10
56秒前
jokerhoney完成签到,获得积分10
58秒前
李娇完成签到 ,获得积分10
59秒前
李姝完成签到 ,获得积分10
1分钟前
李昕123完成签到 ,获得积分10
1分钟前
1分钟前
干净涵梅发布了新的文献求助10
1分钟前
星辰大海应助大喵采纳,获得10
1分钟前
CC发布了新的文献求助10
1分钟前
你大米哥完成签到 ,获得积分10
1分钟前
asaki完成签到,获得积分10
1分钟前
瑞瑞刘完成签到 ,获得积分10
1分钟前
freshfire完成签到 ,获得积分10
1分钟前
爱吃大米饭完成签到 ,获得积分10
1分钟前
1分钟前
yx_cheng应助CC采纳,获得30
1分钟前
小鸟芋圆露露完成签到 ,获得积分10
1分钟前
大喵发布了新的文献求助10
1分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990012
求助须知:如何正确求助?哪些是违规求助? 3532047
关于积分的说明 11256141
捐赠科研通 3270918
什么是DOI,文献DOI怎么找? 1805105
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809216