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MRI features predict p53 status in lower-grade gliomas via a machine-learning approach
Li, Yiming1; Qian, Zenghui1; Xu, Kaibin2; Wang, Kai3; Fan, Xing1; Li, Shaowu4; Jiang, Tao1,5,6,7; Liu, Xing1; Wang, Yinyan5
Source PublicationNEUROIMAGE-CLINICAL
2018
Volume17Pages:306-311
SubtypeArticle
AbstractBackground: 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.
KeywordP53 Lower-grade Gliomas Radiogenomics Prediction Machine Learning
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.nicl.2017.10.030
WOS KeywordENDOTHELIAL GROWTH-FACTOR ; SQUAMOUS-CELL CARCINOMA ; TEXTURE FEATURES ; SURVIVAL ; CANCER ; EXPRESSION ; MUTATIONS ; PROGNOSIS ; SELECTION ; TUMORS
Indexed BySCI
Language英语
Funding OrganizationNational 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)
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeuroimaging
WOS IDWOS:000426180300033
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21965
Collection脑网络组研究中心
Affiliation1.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
Recommended Citation
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.
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