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Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images
Sun, Kai1,2; Liu, Zhenyu2; Li, Yiming3; Wang, Lei3; Tang, Zhenchao2,4; Wang, Shuo2,4; Zhou, Xuezhi1,2; Shao, Lizhi2,5; Sun, Caixia2,6; Liu, Xing3; Jiang, Tao3; Wang, Yinyan3; Tian, Jie1,2,4,7
发表期刊FRONTIERS IN ONCOLOGY
ISSN2234-943X
2020-07-08
卷号10页码:8
通讯作者Wang, Yinyan(tiantanyinyan@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要Purpose:The present study aimed to evaluate the performance of radiomics features in the preoperative prediction of epileptic seizure following surgery in patients with LGG. Methods:This retrospective study collected 130 patients with LGG. Radiomics features were extracted from the T2-weighted MR images obtained before surgery. Multivariable Cox-regression with two nested leave-one-out cross validation (LOOCV) loops was applied to predict the prognosis, and elastic net was used in each LOOCV loop to select the predictive features. Logistic models were then built with the selected features to predict epileptic seizures at two time points. Student'st-tests were then used to compare the logistic model predicted probabilities of developing epilepsy in the epilepsy and non-epilepsy groups. Thet-test was used to identify features that differentiated patients with early-onset epilepsy from their late-onset counterparts. Results:Seventeen features were selected with the two nested LOOCV loops. The index of concordance (C-index) of the Cox model was 0.683, and the logistic model predicted probabilities of seizure were significantly different between the epilepsy and non-epilepsy groups at each time point. Moreover, one feature was found to be significantly different between the patients with early- or late-onset epilepsy. Conclusion:A total of 17 radiomics features were correlated with postoperative epileptic seizures in patients with LGG and one feature was a significant predictor of the time of epilepsy onset.
关键词low-grade glioma epilepsy radiomics elastic net Cox regression
DOI10.3389/fonc.2020.01096
关键词[WOS]MULTIVARIATE PATTERN-ANALYSIS ; BRAIN-TUMORS ; SURVIVAL ; PROPHYLAXIS ; PREDICTION ; REGRESSION ; RISK
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81772012] ; Beijing Natural Science Foundation[7182109] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; National Key R&D Program of China[2016YFA0100902] ; Strategic Priority Research Programof Chinese Academy of Sciences[XDB32030200] ; Strategic Priority Research Programof Chinese Academy of Sciences[XDB01030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[SFH 2018-2-1072] ; Youth Innovation Promotion Association CAS[2019136]
项目资助者National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Strategic Priority Research Programof Chinese Academy of Sciences ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS研究方向Oncology
WOS类目Oncology
WOS记录号WOS:000553869300001
出版者FRONTIERS MEDIA SA
引用统计
被引频次:10[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/40296
专题中国科学院分子影像重点实验室
通讯作者Wang, Yinyan; Tian, Jie
作者单位1.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Peoples R China
2.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Capital Med Univ, Beijing Tiantan Hosp, Beijing, Peoples R China
4.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
5.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
6.Guizhou Univ, Sch Comp Sci & Technol, Key Lab Intelligent Med Image Anal & Precise Diag, Guiyang, Peoples R China
7.Univ Chinese Acad Sci, Beijing, Peoples R China
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Sun, Kai,Liu, Zhenyu,Li, Yiming,et al. Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images[J]. FRONTIERS IN ONCOLOGY,2020,10:8.
APA Sun, Kai.,Liu, Zhenyu.,Li, Yiming.,Wang, Lei.,Tang, Zhenchao.,...&Tian, Jie.(2020).Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images.FRONTIERS IN ONCOLOGY,10,8.
MLA Sun, Kai,et al."Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images".FRONTIERS IN ONCOLOGY 10(2020):8.
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