CASIA OpenIR
Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study
Zhang, Lu1,2; Dong, Di3,4; Li, Hailin3,4,5; Tian, Jie3,4; Ouyang, Fusheng2; Mo, Xiaokai1; Zhang, Bin6,7; Luo, Xiaoning1,2; Lian, Zhouyang1; Pei, Shufang1; Dong, Yuhao1; Huang, Wenhui1; Liang, Changhong1; Liu, Jing1; Zhang, Shuixing1
Source PublicationEBIOMEDICINE
ISSN2352-3964
2019-02-01
Volume40Pages:327-335
Corresponding AuthorZhang, Shuixing(shui7515@126.com)
AbstractBackground: We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC). Methods: This retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment. Subsequently, we used minimum redundancy-maximum relevance and least absolute shrinkage and selection operator algorithms to select the strongest features and build a logistic model for DM prediction. The independent statistical significance of multiple clinical variables was tested using multivariate logistic regression analysis. Findings: In total, 2780 radiomic features were extracted. A DM MRI-based model (DMMM) comprising seven features was constructed for the classification of patients into high-and low-risk groups in a training cohort and validated in an independent cohort. Overall survival was significantly shorter in the high-risk group than in the low-risk group (P < 0.001). A radiomics nomogram based on radiomic features and clinical variables was developed for DM risk assessment in each patient, and it showed a significant predictive ability in the training [area under the curve (AUC), 0.827; 95% confidence interval (CI), 0.754-0.900] and validation (AUC, 0.792; 95% CI, 0.633-0.952) cohorts. Interpretation: DMMM can serve as a visual prognostic tool for DM prediction in NPC, and it can improve treatment decisions by aiding in the differentiation of patients with high and low risks of DM. (C) 2019 Published by Elsevier B.V.
KeywordDistant metastasis Nasopharyngeal carcinoma Risk assessment Prognostic tool Magnetic resonance imaging
DOI10.1016/j.ebiom.2019.01.013
WOS KeywordRADIOMICS ; FEATURES ; RISK ; CHEMOTHERAPY ; SIGNATURE ; MULTICENTER ; MRI
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81571664] ; National Natural Science Foundation of China[81871323] ; National Natural Science Foundation of China[81801665] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of Guangdong Province[2018B030311024] ; Science and Technology Planning Project of Guangdong Province[2016A020216020] ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission[201707010328] ; China Postdoctoral Science Foundation[2016M600145] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFC1309100]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of Guangdong Province ; Science and Technology Planning Project of Guangdong Province ; Scientific Research General Project of Guangzhou Science Technology and Innovation Commission ; China Postdoctoral Science Foundation ; National Key R&D Program of China
WOS Research AreaGeneral & Internal Medicine ; Research & Experimental Medicine
WOS SubjectMedicine, General & Internal ; Medicine, Research & Experimental
WOS IDWOS:000460696900045
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25003
Collection中国科学院自动化研究所
Corresponding AuthorZhang, Shuixing
Affiliation1.Guangdong Acad Med Sci, Guangdong Gen Hosp, Dept Radiol, 106 Zhongshan Er Rd, Guangzhou 510080, Guangdong, Peoples R China
2.Southern Med Univ, Grad Coll, Guangzhou, Guangdong, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
6.Jinan Univ, Affiliated Hosp 1, Med Imaging Ctr, Guangzhou, Guangdong, Peoples R China
7.Jinan Univ, Inst Mol & Funct Imaging, Guangzhou, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Lu,Dong, Di,Li, Hailin,et al. Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study[J]. EBIOMEDICINE,2019,40:327-335.
APA Zhang, Lu.,Dong, Di.,Li, Hailin.,Tian, Jie.,Ouyang, Fusheng.,...&Zhang, Shuixing.(2019).Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study.EBIOMEDICINE,40,327-335.
MLA Zhang, Lu,et al."Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study".EBIOMEDICINE 40(2019):327-335.
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