兰州大学机构库
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; Liu, Zaiyi4; Zhou, Junlin3; Tian, Jie1,5,6,7
2022-08-01
Source PublicationNeural Networks
ISSN0893-6080
Volume152Pages:394-406
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. © 2022 Elsevier Ltd
KeywordDeep neural networks Diseases Forecasting Computed tomography Computed tomography images Deep learning Human cancer Learning methods Multi tasks Neural-networks Overall survival Risk predictions Task networks
PublisherElsevier Ltd
DOI10.1016/j.neunet.2022.04.027
Indexed ByEI
Language英语
EI Accession Number20222212174422
EI KeywordsComputerized tomography
EI Classification Number461.4 Ergonomics and Human Factors Engineering ; 723.5 Computer Applications
Original Document TypeJournal article (JA)
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/481935
Collection兰州大学
Corresponding AuthorDong, Di; Liu, Zaiyi
Affiliation1.CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing; 100190, China;
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing; 100049, China;
3.Department of Radiology, Lanzhou University Second Hospital, Lanzhou; 730030, China;
4.Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou; 510080, China;
5.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing; 100191, China;
6.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Shaanxi, Xi'an; 710126, China;
7.Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing; 100191, 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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