CASIA OpenIR  > 中国科学院分子影像重点实验室
Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features
Zhou, Hongyu1,2,4; Dong, Di2,4; Chen, Bojiang3; Fang, Mengjie2; Cheng, Yue3; Gan, Yuncun; Zhang, Rui3; Zhang, Liwen2; Zang, Yali2; Liu, Zhenyu2; Zheng, Hairong1; Li, Weimin3; Tian, Jie2,4
Source PublicationTRANSLATIONAL ONCOLOGY
2018-02-01
Volume11Issue:1Pages:31-36
SubtypeArticle
AbstractOBJECTIVES: To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer. METHODS: This was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature. RESULTS: Four radiomic features and three clinical features were obtained by FSV feature selection. Classification accuracy by the proposed SVM with SGD method was 71.02%, and the area under the curve was 72.84% with only the radiomic features extracted from CT. After the addition of clinical features, 89.09% can be achieved. CONCLUSION: The radiomic features of the pretherapy CT images may be used as predictors of distant metastasis. And it also can be used in combination with the patient's gender and tumor T and N phase information to diagnose the possibility of distant metastasis in lung cancer.
KeywordRadiomics
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.tranon.2017.10.010
WOS KeywordIMAGES ; CT ; PREDICTION ; SIGNATURE ; SURVIVAL ; DISEASE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(81227901 ; National Key R&D Program of China(2017YFA0205200 ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences(KFJ-SW-STS-160) ; Instrument Developing Project of the Chinese Academy of Sciences(YZ201502) ; Beijing Municipal Science and Technology Commission(Z161100002616022) ; Key Program from the Department of Science and Technology, Sichuan Province, China(2017SZ0052) ; Youth Innovation Promotion Association CAS ; 81771924 ; 2017YFC1308700 ; 81501616 ; 2017YFC1308701 ; 61231004 ; 2017YFC1309100) ; 81671851 ; 81527805)
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000423454900005
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20308
Collection中国科学院分子影像重点实验室
Affiliation1.Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, 1068 Xueyuan Ave, Shenzhen 518055, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Sichuan Univ, West China Hosp, Dept Resp & Crit Care Med, Chengdu 610041, Sichuan, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Zhou, Hongyu,Dong, Di,Chen, Bojiang,et al. Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features[J]. TRANSLATIONAL ONCOLOGY,2018,11(1):31-36.
APA Zhou, Hongyu.,Dong, Di.,Chen, Bojiang.,Fang, Mengjie.,Cheng, Yue.,...&Tian, Jie.(2018).Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features.TRANSLATIONAL ONCOLOGY,11(1),31-36.
MLA Zhou, Hongyu,et al."Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features".TRANSLATIONAL ONCOLOGY 11.1(2018):31-36.
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