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

doi:10.1016/j.ebiom.2019.01.013

	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, Lu 1,2; Dong, Di3,4 ; Li, Hailin 3,4,5; Tian, Jie3,4 ; Ouyang, Fusheng 2; Mo, Xiaokai 1; Zhang, Bin 6,7; Luo, Xiaoning 1,2; Lian, Zhouyang 1; Pei, Shufang 1
刊名	EBIOMEDICINE
	2019-02-01
卷号	40 页码:327-335
关键词	Distant metastasis Nasopharyngeal carcinoma Risk assessment Prognostic tool Magnetic resonance imaging
ISSN号	2352-3964
DOI	10.1016/j.ebiom.2019.01.013
通讯作者	Zhang, Shuixing(shui7515@126.com)
英文摘要	Background: 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.
资助项目	National 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]
WOS关键词	RADIOMICS ; FEATURES ; RISK ; CHEMOTHERAPY ; SIGNATURE ; MULTICENTER ; MRI
WOS研究方向	General & Internal Medicine ; Research & Experimental Medicine
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000460696900045
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/25002]
专题	中国科学院自动化研究所
通讯作者	Zhang, Shuixing
作者单位	1.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
推荐引用方式 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.