Knowledge Commons of Institute of Automation，CAS
| 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 Publication | EBIOMEDICINE
 |
| ISSN | 2352-3964 |
| 2019-02-01 | |
| Volume | 40Pages:327-335 |
| Corresponding Author | Zhang, Shuixing(shui7515@126.com) |
| Abstract | 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. |
| Keyword | Distant metastasis Nasopharyngeal carcinoma Risk assessment Prognostic tool Magnetic resonance imaging |
| DOI | 10.1016/j.ebiom.2019.01.013 |
| WOS Keyword | RADIOMICS ; FEATURES ; RISK ; CHEMOTHERAPY ; SIGNATURE ; MULTICENTER ; MRI |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | General & Internal Medicine ; Research & Experimental Medicine |
| WOS Subject | Medicine, General & Internal ; Medicine, Research & Experimental |
| WOS ID | WOS:000460696900045 |
| Publisher | ELSEVIER SCIENCE BV |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/25003 |
| Collection | 中国科学院自动化研究所 |
| Corresponding Author | Zhang, Shuixing |
| Affiliation | 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 |
| 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment