CASIA OpenIR  > 中国科学院分子影像重点实验室
Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas
Liu zhenyu; Wang Yinyan; Liu Xing; Du Yang; Tang zhenchao; Wang Kai; Wei Jingwei; Dong Di; Zang yali; Dai Jianping; Jiang Tao; Tian Jie
Source PublicationNeuroimage:Clinical
2018-04-24
Issue19Pages:271-278
Abstract

Purpose: To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy. Methods: This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the in­teractions between these two kinds of features were analyzed. Elastic net was applied to generate a radiomics signature combining key imaging features associated with the LGG-related epilepsy with the primary cohort, and then a nomogram incorporating radiomics signature and clinical characteristics was developed. The radiomics signature and nomogram were validated in the validation cohort. Results: A total of 475 features associated with LGG-related epilepsy were obtained for each patient. A radiomics signature with eleven selected features allowed for discriminating patients with epilepsy or not was detected, which performed better than location and 3-D imaging features. The nomogram incorporating radiomics sig­nature and clinical characteristics achieved a high degree of discrimination with area under receiver operating characteristic (ROC) curve (AUC) at 0.8769 in the primary cohort and 0.8152 in the validation cohort. The nomogram also allowed for good calibration in the primary cohort. Conclusion: We developed and validated an eective prediction model for LGG-related epilepsy. Our results suggested that radiomics analysis may enable more precise and individualized prediction of LGG-related epi­lepsy.

KeywordLow Grade Gliomas, Radiomics, Epilepsy, Elastic Net, T2wi
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23184
Collection中国科学院分子影像重点实验室
Corresponding AuthorJiang Tao; Tian Jie
Recommended Citation
GB/T 7714
Liu zhenyu,Wang Yinyan,Liu Xing,et al. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas[J]. Neuroimage:Clinical,2018(19):271-278.
APA Liu zhenyu.,Wang Yinyan.,Liu Xing.,Du Yang.,Tang zhenchao.,...&Tian Jie.(2018).Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas.Neuroimage:Clinical(19),271-278.
MLA Liu zhenyu,et al."Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas".Neuroimage:Clinical .19(2018):271-278.
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