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

文献类型：期刊论文

作者Li, Yiming2; Liang, Yuchao3; Sun, Zhiyan2; Xu, Kaibin4; Fan, Xing2; Li, Shaowu1; Zhang, Zhong3; Jiang, Tao5,6,7; Liu, Xing2; Wang, Yinyan3
刊名NEURORADIOLOGY
出版日期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
DOI10.1007/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.
WOS关键词MRI FEATURES ; TUMOR ; CLASSIFICATION ; GLIOMAS ; GENE ; EXPRESSION ; BIOMARKERS ; COMPLEXES ; PATHWAYS ; TEXTURE
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研究方向Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000503025800003
出版者SPRINGER
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
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.Capital Med Univ, Beijing Neurosurg Inst, Neurol Imaging Ctr, Beijing, Peoples R China
2.Capital Med Univ, Beijing Neurosurg Inst, Beijing, Peoples R China
3.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, 6 Tiantanxili, Beijing 100050, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
5.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China
6.Asian Glioma Genome Atlas Network AGGA, Beijing, Peoples R China
7.Chinese Glioma Genome Atlas Network CGGA, 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.

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来源：自动化研究所

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