CASIA OpenIR  > 中国科学院分子影像重点实验室
Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography images
Zhang, Liwen1,2; Zhong, Lianzhen1,2; Li, Cong1; Zhang, Wenjuan3; Hu, Chaoen1; Dong, Di1,2,8; Liu, Zaiyi4; Zhou, Junlin3; Tian, Jie1,5,6,7,8
Source PublicationNEURAL NETWORKS
ISSN0893-6080
2022-08-01
Volume152Pages:394-406
Corresponding AuthorDong, 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)
AbstractAccurate 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.
KeywordOverall survival Deep learning Computed tomography (CT) Neural network
DOI10.1016/j.neunet.2022.04.027
WOS KeywordGASTRIC-CANCER
Indexed BySCI
Language英语
Funding ProjectMinistry 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]
Funding OrganizationMinistry 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 Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000807785500011
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49621
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong, Di; Liu, Zaiyi; Zhou, Junlin; Tian, Jie
Affiliation1.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
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
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.
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