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Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients

文献类型：期刊论文

作者Zhang, Liwen5,6; Dong, Di5,6; Zhong, Lianzhen5,6; Li, Cong5,6; Hu, Chaoen6; Yang, Xin6; Liu, Zaiyi4; Wang, Rongpin1; Zhou, Junlin3; Tian, Jie2,6
刊名IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
出版日期2021-10-01
卷号25期号:10页码:3933-3942
ISSN号2168-2194
关键词Hazards Feature extraction Computed tomography Cancer Radiomics Indexes Bioinformatics Overall survival gastric cancer multi-level CT image deep learning
DOI10.1109/JBHI.2021.3087634
通讯作者Liu, Zaiyi(zyliu@163.com) ; Wang, Rongpin(wangrongpin@126.com) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Tian, Jie(tian@ieee.org)
英文摘要Gastric cancer (GC) is the third leading cause of cancer-associated deaths globally. Accurate risk prediction of the overall survival (OS) for GC patients shows significant prognostic value, which helps identify and classify patients into different risk groups to benefit from personalized treatment. Many methods based on machine learning algorithms have been widely explored to predict the risk of OS. However, the accuracy of risk prediction has been limited and remains a challenge with existing methods. Few studies have proposed a framework and pay attention to the low-level and high-level features separately for the risk prediction of OS based on computed tomography images of GC patients. To achieve high accuracy, we propose a multi-focus fusion convolutional neural network. The network focuses on low-level and high-level features, where a subnet to focus on lower-level features and the other enhanced subnet with lateral connection to focus on higher-level semantic features. Three independent datasets of 640 GC patients are used to assess our method. Our proposed network is evaluated by metrics of the concordance index and hazard ratio. Our network outperforms state-of-the-art methods with the highest concordance index and hazard ratio in independent validation and test sets. Our results prove that our architecture can unify the separate low-level and high-level features into a single framework, and can be a powerful method for accurate risk prediction of OS.
WOS关键词RADIOMICS ; BRIDGE
资助项目Strategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0700401] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[62027901] ; Beijing Natural Science Foundation[L182061] ; Project of High-Level Talents Team Introduction in Zhuhai City Zhuhai[HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175]
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000704111100029
资助机构Strategic Priority Research Program of Chinese Academy of Sciences ; National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Project of High-Level Talents Team Introduction in Zhuhai City Zhuhai ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/46173]  
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Liu, Zaiyi; Wang, Rongpin; Zhou, Junlin; Tian, Jie
作者单位1.Guizhou Prov Peoples Hosp, Dept Radiol, Guiyang 550002, Peoples R China
2.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
3.Lanzhou Univ, Hosp 2, Dept Radiol, Lanzhou 730030, Peoples R China
4.Guangdong Gen Hosp, Dept Radiol, Guangzhou 510080, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Liwen,Dong, Di,Zhong, Lianzhen,et al. Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2021,25(10):3933-3942.
APA Zhang, Liwen.,Dong, Di.,Zhong, Lianzhen.,Li, Cong.,Hu, Chaoen.,...&Tian, Jie.(2021).Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,25(10),3933-3942.
MLA Zhang, Liwen,et al."Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 25.10(2021):3933-3942.

入库方式： OAI收割

来源：自动化研究所

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