Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images

doi:10.1016/j.neunet.2022.04.027

	Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images
	Zhang, Liwen1,2 ; Zhong, Lianzhen 1,2; Li, Cong1 ; Zhang, Wenjuan 3; Hu, Chaoen1 ; Dong, Di1,2,8 ; Liu, Zaiyi 4; Zhou, Junlin 3; Tian, Jie 1,5,6,7,8
发表期刊	NEURAL NETWORKS
ISSN	0893-6080
	2022-08-01
卷号	152 页码:394-406
通讯作者	Dong, Di(di.dong@ia.ac.cn) ; Liu, Zaiyi(zyliu@163.com) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	Accurate preoperative prediction of overall survival (OS) risk of human cancers based on CT images is greatly significant for personalized treatment. Deep learning methods have been widely explored to improve automated prediction of OS risk. However, the accuracy of OS risk prediction has been limited by prior existing methods. To facilitate capturing survival-related information, we proposed a novel knowledge-guided multi-task network with tailored attention modules for OS risk prediction and prediction of clinical stages simultaneously. The network exploits useful information contained in multiple learning tasks to improve prediction of OS risk. Three multi-center datasets, including two gastric cancer datasets with 459 patients, and a public American lung cancer dataset with 422 patients, are used to evaluate our proposed network. The results show that our proposed network can boost its performance by capturing and sharing information from other predictions of clinical stages. Our method outperforms the state-of-the-art methods with the highest geometrical metric. Furthermore, our method shows better prognostic value with the highest hazard ratio for stratifying patients into high-and low-risk groups. Therefore, our proposed method may be exploited as a potential tool for the improvement of personalized treatment. (C) 2022 Elsevier Ltd. All rights reserved.
关键词	Overall survival Deep learning Computed tomography (CT) Neural network
DOI	10.1016/j.neunet.2022.04.027
关键词[WOS]	GASTRIC-CANCER
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[82022036] ; 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[6202790004] ; National Natural Science Foundation of China[81930053] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application[2022B1212010011] ; Special Foundation of State Key Laboratory of Complex Systems Management and Control[2022QN03] ; Youth Innovation Promotion Association CAS[2017175]
项目资助者	Ministry of Science and Technology of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; Strategic Priority Research Program of Chinese Academy of Sciences ; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application ; Special Foundation of State Key Laboratory of Complex Systems Management and Control ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science ; Neurosciences & Neurology
WOS类目	Computer Science, Artificial Intelligence ; Neurosciences
WOS记录号	WOS:000807785500011
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：4[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/173211/49621
专题	中国科学院分子影像重点实验室
通讯作者	Dong, Di; Liu, Zaiyi; Zhou, Junlin; Tian, Jie
作者单位	1.Inst Automation, Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Manageme, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Lanzhou Univ, Hosp 2, Dept Radiol, Lanzhou 730030, Peoples R China 4.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Guangdong Prov Key Lab Artificial Intelligence Med, Guangzhou 510080, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China 6.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xi'an 710126, Shaanxi, Peoples R China 7.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China 8.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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,Zhong, Lianzhen,Li, Cong,et al. Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images[J]. NEURAL NETWORKS,2022,152:394-406.
APA	Zhang, Liwen.,Zhong, Lianzhen.,Li, Cong.,Zhang, Wenjuan.,Hu, Chaoen.,...&Tian, Jie.(2022).Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images.NEURAL NETWORKS,152,394-406.
MLA	Zhang, Liwen,et al."Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images".NEURAL NETWORKS 152(2022):394-406.