Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| Multi-Focus Network to Decode Imaging Phenotype for Overall Survival Prediction of Gastric Cancer Patients | |
Zhang, Liwen1,2 ; Dong, Di1,2; Zhong, Lianzhen1,2; Li, Cong1,2; Hu, Chaoen1; Yang, Xin1; Liu, Zaiyi3; Wang, Rongpin4; Zhou, Junlin5; Tian, Jie1,6
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| Source Publication | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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| ISSN | 2168-2194 |
| 2021-10-01 | |
| Volume | 25Issue:10Pages:3933-3942 |
| Corresponding Author | Liu, Zaiyi(zyliu@163.com) ; Wang, Rongpin(wangrongpin@126.com) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn) ; Tian, Jie(tian@ieee.org) |
| Abstract | 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. |
| Keyword | 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 |
| WOS Keyword | RADIOMICS ; BRIDGE |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 Research Area | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics |
| WOS ID | WOS:000704111100029 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 医学影像处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/46174 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Liu, Zaiyi; Wang, Rongpin; Zhou, Junlin; Tian, Jie |
| Affiliation | 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 |
| First Author Affilication | Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
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