鼻咽癌放射组学与深度学习:综述

IF 17.2 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL IEEE Reviews in Biomedical Engineering Pub Date : 2023-04-25 DOI:10.1109/RBME.2023.3269776
Zipei Wang;Mengjie Fang;Jie Zhang;Linquan Tang;Lianzhen Zhong;Hailin Li;Runnan Cao;Xun Zhao;Shengyuan Liu;Ruofan Zhang;Xuebin Xie;Haiqiang Mai;Sufang Qiu;Jie Tian;Di Dong
{"title":"鼻咽癌放射组学与深度学习:综述","authors":"Zipei Wang;Mengjie Fang;Jie Zhang;Linquan Tang;Lianzhen Zhong;Hailin Li;Runnan Cao;Xun Zhao;Shengyuan Liu;Ruofan Zhang;Xuebin Xie;Haiqiang Mai;Sufang Qiu;Jie Tian;Di Dong","doi":"10.1109/RBME.2023.3269776","DOIUrl":null,"url":null,"abstract":"Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.2000,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review\",\"authors\":\"Zipei Wang;Mengjie Fang;Jie Zhang;Linquan Tang;Lianzhen Zhong;Hailin Li;Runnan Cao;Xun Zhao;Shengyuan Liu;Ruofan Zhang;Xuebin Xie;Haiqiang Mai;Sufang Qiu;Jie Tian;Di Dong\",\"doi\":\"10.1109/RBME.2023.3269776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.\",\"PeriodicalId\":39235,\"journal\":{\"name\":\"IEEE Reviews in Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":17.2000,\"publicationDate\":\"2023-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Reviews in Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10107769/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Reviews in Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10107769/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

鼻咽癌是一种常见的头颈部恶性肿瘤,与其他类型的癌症相比,其临床治疗方法截然不同。精准的风险分层和量身定制的治疗干预对改善生存结果至关重要。人工智能,包括放射组学和深度学习,在鼻咽癌的各种临床任务中表现出了相当大的功效。这些技术利用医学影像和其他临床数据来优化临床工作流程,最终使患者受益。在本综述中,我们将概述放射组学和深度学习在医学图像分析中的技术方面和基本工作流程。然后,我们详细回顾了它们在鼻咽癌临床诊断和治疗的七项典型任务中的应用,涵盖了图像合成、病灶分割、诊断和预后等各个方面。总结了前沿研究的创新和应用效果。认识到研究领域的异质性以及研究与临床转化之间存在的差距,讨论了潜在的改进途径。我们提出,可以通过建立标准化的大型数据集、探索特征的生物学特性以及技术升级来逐步解决这些问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review
Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.
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
IEEE Reviews in Biomedical Engineering
IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
31.70
自引率
0.60%
发文量
93
期刊介绍: IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.
期刊最新文献
A Survey of Few-Shot Learning for Biomedical Time Series. The Physiome Project and Digital Twins. Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey. Data- and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies. Exhaled Breath Analysis: from Laboratory Test to Wearable Sensing.
×
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