Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging

doi:10.1007/s00234-019-02244-7

CORC > 自动化研究所 > 中国科学院自动化研究所 > 综合信息系统研究中心 > 脑机融合与认知评估

	Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging
	Li, Yiming 6; Liang, Yuchao 5; Sun, Zhiyan 6; Xu, Kaibin 1; Fan, Xing 6; Li, Shaowu 7; Zhang, Zhong 5; Jiang, Tao 2,3,4; Liu, Xing 6; Wang, Yinyan 5
刊名	NEURORADIOLOGY
	2019-11-01
卷号	61 期号:11 页码:1229-1237
关键词	Glioblastoma Radiogenomics Phosphatase and tensin homolog (PTEN) Machine learning
ISSN号	0028-3940
DOI	10.1007/s00234-019-02244-7
通讯作者	Wang, Yinyan(tiantanyinyan@126.com)
英文摘要	Purpose PTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature. Methods In this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis. Results Six features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature. Conclusion These results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker.
资助项目	National Natural Science Foundation of China[81601452] ; Beijing Natural Science Foundation[7174295] ; National Key Research and Development Plan[2016YFC0902500] ; National Key Research and Development Program of China[2018YFC0115604]
WOS关键词	MRI FEATURES ; TUMOR ; CLASSIFICATION ; GLIOMAS ; GENE ; EXPRESSION ; BIOMARKERS ; COMPLEXES ; PATHWAYS ; TEXTURE
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000503025800003
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29425]
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	Wang, Yinyan
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Chinese Glioma Genome Atlas Network CGGA, Beijing, Peoples R China 3.Asian Glioma Genome Atlas Network AGGA, Beijing, Peoples R China 4.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China 5.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, 6 Tiantanxili, Beijing 100050, Peoples R China 6.Capital Med Univ, Beijing Neurosurg Inst, Beijing, Peoples R China 7.Capital Med Univ, Beijing Neurosurg Inst, Neurol Imaging Ctr, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Yiming,Liang, Yuchao,Sun, Zhiyan,et al. Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging[J]. NEURORADIOLOGY,2019,61(11):1229-1237.
APA	Li, Yiming.,Liang, Yuchao.,Sun, Zhiyan.,Xu, Kaibin.,Fan, Xing.,...&Wang, Yinyan.(2019).Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.NEURORADIOLOGY,61(11),1229-1237.
MLA	Li, Yiming,et al."Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging".NEURORADIOLOGY 61.11(2019):1229-1237.