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Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas

文献类型：期刊论文

作者Sun, Zhiyan5; Li, Yiming5; Wang, Yinyan6; Fan, Xing5; Xu, Kaibin7; Wang, Kai8; Li, Shaowu5; Zhang, Zhong6; Jiang, Tao1,2,3,4,5,6; Liu, Xing5
刊名CANCER IMAGING
出版日期2019-10-21
卷号19期号:1页码:8
关键词Vascular endothelial growth factor Diffuse gliomas Radiomic analysis Machine learning
ISSN号1740-5025
DOI10.1186/s40644-019-0256-y
通讯作者Liu, Xing(15846591696@126.com)
英文摘要Objective To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis. Materials and methods Preoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II-IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed. Results Nine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups. Conclusions Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.
WOS关键词CENTRAL-NERVOUS-SYSTEM ; FACTOR EXPRESSION ; RADIOMIC FEATURES ; GRADE GLIOMAS ; TUMORS ; BRAIN ; ASSOCIATIONS ; MUTATIONS ; SURVIVAL ; TEXTURE
资助项目National Natural Science Foundation of China[81601452] ; Beijing Natural Science Foundation[7174295] ; National Key Research and Development Plan[2016YFC0902500]
WOS研究方向Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000492028400002
出版者BMC
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan
源URL[http://ir.ia.ac.cn/handle/173211/28854]  
专题综合信息系统研究中心_脑机融合与认知评估
通讯作者Liu, Xing
作者单位1.Asian Glioma Genome Atlas Network AGGA, Beijing, Peoples R China
2.Beijing Inst Brain Disorders, Ctr Brain Tumor, Beijing, Peoples R China
3.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China
4.Chinese Glioma Genome Atlas Network CGGA, Beijing, Peoples R China
5.Capital Med Univ, Beijing Neurosurg Inst, 6 Tiantanxili, Beijing 100050, Peoples R China
6.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
8.Capital Med Univ, Beijing Tiantan Hosp, Dept Nucl Med, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Sun, Zhiyan,Li, Yiming,Wang, Yinyan,et al. Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas[J]. CANCER IMAGING,2019,19(1):8.
APA Sun, Zhiyan.,Li, Yiming.,Wang, Yinyan.,Fan, Xing.,Xu, Kaibin.,...&Liu, Xing.(2019).Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas.CANCER IMAGING,19(1),8.
MLA Sun, Zhiyan,et al."Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas".CANCER IMAGING 19.1(2019):8.

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来源：自动化研究所

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