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Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review

文献类型：期刊论文

作者Wang, Zipei1,2,3; Fang, Mengjie4,5; Zhang, Jie6; Tang, Linquan1,7,8; Zhong, Lianzhen5; Li, Hailin6; Cao, Runnan1; Zhao, Xun1; Liu, Shengyuan1; Zhang, Ruofan1
刊名IEEE REVIEWS IN BIOMEDICAL ENGINEERING
出版日期2024
卷号17页码:118-135
关键词Artificial intelligence Medical diagnostic imaging Image segmentation medical imaging nasopharyngeal carcinoma precision diagnosis and treatment
ISSN号1937-3333
DOI10.1109/RBME.2023.3269776
通讯作者Xie, Xuebin(xiexuebin64@163.com) ; Tian, Jie(tian@ieee.org) ; Dong, Di(di.dong@ia.ac.cn)
英文摘要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.
WOS关键词PLUS CONCURRENT CHEMORADIOTHERAPY ; INTENSITY-MODULATED RADIOTHERAPY ; CLINICAL TARGET VOLUME ; MRI-BASED RADIOMICS ; RADIATION-THERAPY ; ADJUVANT CHEMOTHERAPY ; TEXTURE ANALYSIS ; CERVICAL-SPINE ; RANDOM FOREST ; CANCER
资助项目Chinese Academy of Sciences
WOS研究方向Engineering
语种英语
WOS记录号WOS:001166967200004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/57948]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Xie, Xuebin; Tian, Jie; Dong, Di
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Beijing Univ Posts & Telecommun, Sch Modern Post, Sch Automat, Beijing 100876, Peoples R China
4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
5.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China
6.Jinan Univ, Zhuhai Peoples Hosp, Dept Radiol, Zhuhai Hosp, Zhuhai, Peoples R China
7.Sun Yat Sen UniversityCancer Ctr, Col laborat Innovat Ctr Canc Med, State Key Lab Oncol South China, Guangdong Key Labo ratory Nasopharyngeal Carcinoma, Guangzhou 510060, Peoples R China
8.Sun Yat Sen Univ, Dept Nasopharyngeal Carcinoma, Canc Ctr, Guangzhou 510060, Peoples R China
9.Kiangwu Hosp, Macau 999078, Peoples R China
10.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China,Canc Ctr, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Guangzhou 510060, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zipei,Fang, Mengjie,Zhang, Jie,et al. Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review[J]. IEEE REVIEWS IN BIOMEDICAL ENGINEERING,2024,17:118-135.
APA Wang, Zipei.,Fang, Mengjie.,Zhang, Jie.,Tang, Linquan.,Zhong, Lianzhen.,...&Dong, Di.(2024).Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.IEEE REVIEWS IN BIOMEDICAL ENGINEERING,17,118-135.
MLA Wang, Zipei,et al."Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review".IEEE REVIEWS IN BIOMEDICAL ENGINEERING 17(2024):118-135.

入库方式： OAI收割

来源：自动化研究所

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