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

doi:10.1109/JBHI.2021.3087634

	Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients
	Zhang, Liwen1,2 ; Dong, Di1,2 ; Zhong, Lianzhen 1,2; Li, Cong1,2 ; Hu, Chaoen1 ; Yang, Xin1 ; Liu, Zaiyi 3; Wang, Rongpin 4; Zhou, Junlin 5; Tian, Jie 1,6
发表期刊	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN	2168-2194
	2021-10-01
卷号	25 期号:10 页码:3933-3942
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)
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.
关键词	Hazards Feature extraction Computed tomography Cancer Radiomics Indexes Bioinformatics Overall survival gastric cancer multi-level CT image deep learning
DOI	10.1109/JBHI.2021.3087634
关键词[WOS]	RADIOMICS ; BRIDGE
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS类目	Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS记录号	WOS:000704111100029
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46174
专题	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.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Guangdong Gen Hosp, Dept Radiol, Guangzhou 510080, Peoples R China 4.Guizhou Prov Peoples Hosp, Dept Radiol, Guiyang 550002, Peoples R China 5.Lanzhou Univ, Hosp 2, Dept Radiol, Lanzhou 730030, Peoples R China 6.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
第一作者单位	中国科学院分子影像重点实验室
通讯作者单位	中国科学院分子影像重点实验室
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.