Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | NEUROIMAGE-CLINICAL |
2018 | |
卷号 | 17页码:306-311 |
文章类型 | Article |
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. |
关键词 | P53 Lower-grade Gliomas Radiogenomics Prediction Machine Learning |
WOS标题词 | Science & Technology ; Life Sciences & Biomedicine |
DOI | 10.1016/j.nicl.2017.10.030 |
关键词[WOS] | ENDOTHELIAL GROWTH-FACTOR ; SQUAMOUS-CELL CARCINOMA ; TEXTURE FEATURES ; SURVIVAL ; CANCER ; EXPRESSION ; MUTATIONS ; PROGNOSIS ; SELECTION ; TUMORS |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | 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) |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neuroimaging |
WOS记录号 | WOS:000426180300033 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21965 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
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. |
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