MRI features predict p53 status in lower-grade gliomas via a machine-learning approach
文献类型：期刊论文
| 作者 | Li, Yiming1 ; Qian, Zenghui1; Xu, Kaibin2; Wang, Kai3; Fan, Xing1; Li, Shaowu4; Jiang, Tao1,5,6,7; Liu, Xing1; Wang, Yinyan5
|
| 刊名 | NEUROIMAGE-CLINICAL
 |
| 出版日期 | 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) |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。