MRI features predict p53 status in lower-grade gliomas via a machine-learning approach

doi:10.1016/j.nicl.2017.10.030

	MRI features predict p53 status in lower-grade gliomas via a machine-learning approach
	Li, Yiming 1; Qian, Zenghui 1; Xu, Kaibin2 ; Wang, Kai 3; Fan, Xing 1; Li, Shaowu 4; Jiang, Tao 1,5,6,7; Liu, Xing 1; Wang, Yinyan 5
刊名	NEUROIMAGE-CLINICAL
	2018
卷号	17 页码:306-311
关键词	P53 Lower-grade Gliomas Radiogenomics Prediction Machine Learning
DOI	10.1016/j.nicl.2017.10.030
文献子类	Article
英文摘要	Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images.
WOS关键词	ENDOTHELIAL GROWTH-FACTOR ; SQUAMOUS-CELL CARCINOMA ; TEXTURE FEATURES ; SURVIVAL ; CANCER ; EXPRESSION ; MUTATIONS ; PROGNOSIS ; SELECTION ; TUMORS
WOS研究方向	Neurosciences & Neurology
语种	英语
WOS记录号	WOS:000426180300033
资助机构	National Natural Science Foundation of China(81601452) ; Beijing Natural Science Foundation(7174295) ; National Key Research and Development Plan(2016YFC0902500) ; Capital Medical Development Research Fund(2016-1-1072) ; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support(ZYLX201708)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21965]
专题	自动化研究所_脑网络组研究中心
作者单位	1.Capital Med Univ, Beijing Neurosurg Inst, 6 Tiantanxili, Beijing 100050, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Capital Med Univ, Beijing Tiantan Hosp, Dept Neuroradiol, Beijing, Peoples R China 4.Capital Med Univ, Beijing Neurosurg Inst, Neurol Imaging Ctr, Beijing, Peoples R China 5.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing, Peoples R China 6.Beijing Inst Brain Disorders, Ctr Brain Tumor, Beijing, Peoples R China 7.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Yiming,Qian, Zenghui,Xu, Kaibin,et al. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach[J]. NEUROIMAGE-CLINICAL,2018,17:306-311.
APA	Li, Yiming.,Qian, Zenghui.,Xu, Kaibin.,Wang, Kai.,Fan, Xing.,...&Wang, Yinyan.(2018).MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.NEUROIMAGE-CLINICAL,17,306-311.
MLA	Li, Yiming,et al."MRI features predict p53 status in lower-grade gliomas via a machine-learning approach".NEUROIMAGE-CLINICAL 17(2018):306-311.