人工智能在乳腺癌淋巴结转移预测中的应用。

Gabrielle O Windsor, Harrison Bai, Ana P Lourenco, Zhicheng Jiao
{"title":"人工智能在乳腺癌淋巴结转移预测中的应用。","authors":"Gabrielle O Windsor,&nbsp;Harrison Bai,&nbsp;Ana P Lourenco,&nbsp;Zhicheng Jiao","doi":"10.3389/fradi.2023.928639","DOIUrl":null,"url":null,"abstract":"<p><p>Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"3 ","pages":"928639"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364981/pdf/","citationCount":"0","resultStr":"{\"title\":\"Application of artificial intelligence in predicting lymph node metastasis in breast cancer.\",\"authors\":\"Gabrielle O Windsor,&nbsp;Harrison Bai,&nbsp;Ana P Lourenco,&nbsp;Zhicheng Jiao\",\"doi\":\"10.3389/fradi.2023.928639\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.</p>\",\"PeriodicalId\":73101,\"journal\":{\"name\":\"Frontiers in radiology\",\"volume\":\"3 \",\"pages\":\"928639\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364981/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in radiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fradi.2023.928639\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fradi.2023.928639","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

乳腺癌是全球妇女死亡的主要原因。乳腺癌的一个特点是它有能力转移到身体的远处,而这种疾病是通过首先扩散到腋窝淋巴结来实现的。腋窝淋巴结转移的传统诊断包括一种侵入性技术,这可能导致乳腺癌患者的临床并发症。人工智能在医学成像领域的兴起导致了创新的深度学习模型的创建,这些模型可以无创地预测腋窝淋巴结的转移状态,这将导致患者无需进行不必要的活检和解剖。在这篇综述中,我们讨论了各种深度学习人工智能模型在多种成像模式下预测腋窝淋巴结转移的成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Application of artificial intelligence in predicting lymph node metastasis in breast cancer.

Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.20
自引率
0.00%
发文量
0
期刊最新文献
Language task-based fMRI analysis using machine learning and deep learning. Case Report: Diffuse cerebral lymphomatosis with superimposed multifocal primary CNS lymphoma. Diffusion-weighted MRI in the identification of renal parenchymal involvement in children with a first episode of febrile urinary tract infection. SenseCare: a research platform for medical image informatics and interactive 3D visualization. Editorial: Artificial intelligence and multimodal medical imaging data fusion for improving cardiovascular disease care.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1