BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability†

IF 2.6 3区 化学 Q2 CHEMISTRY, ANALYTICAL
Yao Chen, Peiling Wang, Jing Ouyang, Miduo Tan, Libo Nie, Yibo Zhang and Tong Wang
{"title":"BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability†","authors":"Yao Chen, Peiling Wang, Jing Ouyang, Miduo Tan, Libo Nie, Yibo Zhang and Tong Wang","doi":"10.1039/D5AY00475F","DOIUrl":null,"url":null,"abstract":"<p >Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians assess the risk of malignancy and guide appropriate diagnostic and treatment decisions. In this study, an interpretable deep learning model (BIScreener) was proposed for predicting BI-RADS classifications from breast ultrasound images, aiding in the accurate assessment of breast cancer risk and improving diagnostic efficiency. BIScreener utilizes the stacked generalization of three pretrained convolutional neural networks to analyze ultrasound images obtained from two specific instruments (Mindray R5 and HITACHI) used at local hospitals. BIScreener achieved a classification total accuracy of 90.0% and ROC-AUC value of 0.982 in the external test set for five BI-RADS categories. The proposed method achieved 83.8% classification total accuracy and 0.967 ROC-AUC value for seven BI-RADS categories. In addition, the model improved the diagnostic accuracy of two radiologists by more than 8.1% for five BI-RADS categories and by more than 4.8% for seven BI-RADS categories and reduced the explanation time by more than 19.0%, demonstrating its potential to accelerate and improve the breast cancer diagnosis process.</p>","PeriodicalId":64,"journal":{"name":"Analytical Methods","volume":" 27","pages":" 5704-5713"},"PeriodicalIF":2.6000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analytical Methods","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/ay/d5ay00475f","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians assess the risk of malignancy and guide appropriate diagnostic and treatment decisions. In this study, an interpretable deep learning model (BIScreener) was proposed for predicting BI-RADS classifications from breast ultrasound images, aiding in the accurate assessment of breast cancer risk and improving diagnostic efficiency. BIScreener utilizes the stacked generalization of three pretrained convolutional neural networks to analyze ultrasound images obtained from two specific instruments (Mindray R5 and HITACHI) used at local hospitals. BIScreener achieved a classification total accuracy of 90.0% and ROC-AUC value of 0.982 in the external test set for five BI-RADS categories. The proposed method achieved 83.8% classification total accuracy and 0.967 ROC-AUC value for seven BI-RADS categories. In addition, the model improved the diagnostic accuracy of two radiologists by more than 8.1% for five BI-RADS categories and by more than 4.8% for seven BI-RADS categories and reduced the explanation time by more than 19.0%, demonstrating its potential to accelerate and improve the breast cancer diagnosis process.

Abstract Image

BIScreener:通过整合深度学习和可解释性来增强乳腺癌超声诊断。
乳腺癌是全世界妇女死亡的主要原因,通过标准化BI-RADS框架的早期发现有助于医生评估恶性肿瘤的风险,并指导适当的诊断和治疗决策。本研究提出了一种可解释的深度学习模型(BIScreener),用于从乳腺超声图像中预测BI-RADS分类,有助于准确评估乳腺癌风险,提高诊断效率。BIScreener利用三个预训练卷积神经网络的堆叠泛化来分析从当地医院使用的两种特定仪器(Mindray R5和HITACHI)获得的超声图像。在5个BI-RADS类别的外部测试集中,BIScreener的分类总准确率为90.0%,ROC-AUC值为0.982。该方法对7个BI-RADS类别的分类总准确率为83.8%,ROC-AUC值为0.967。此外,该模型将两名放射科医生对5个BI-RADS类别的诊断准确率提高了8.1%以上,将7个BI-RADS类别的诊断准确率提高了4.8%以上,将解释时间缩短了19.0%以上,显示了其加速和改善乳腺癌诊断过程的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Analytical Methods
Analytical Methods CHEMISTRY, ANALYTICAL-FOOD SCIENCE & TECHNOLOGY
CiteScore
5.10
自引率
3.20%
发文量
569
审稿时长
1.8 months
期刊介绍: Early applied demonstrations of new analytical methods with clear societal impact
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信