Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study

doi:10.3389/fonc.2020.00235

	Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study
	Wang, Yinyan 1; Wei, Wei2,3,4,5 ; Liu, Zhenyu2,6 ; Liang, Yuchao 1; Liu, Xing 7; Li, Yiming 1,7; Tang, Zhenchao 2,4; Jiang, Tao 1,7,8,9; Tian, Jie 2,4,5,6
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
	2020-03-13
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 majority of patients with low-grade gliomas (LGGs) experience tumor-related epilepsy during the disease course. Our study aimed to build a radiomic prediction model for LGG-related epilepsy type based on magnetic resonance imaging (MRI) data. Methods: A total of 205 cases with LGG-related epilepsy were enrolled in the retrospective study and divided into training and validation cohorts (1:1) according to their surgery time. Seven hundred thirty-four radiomic features were extracted from T2-weighted imaging, including six location features. Pearson correlation coefficient, univariate area under curve (AUC) analysis, and least absolute shrinkage and selection operator regression were adopted to select the most relevant features for the epilepsy type to build a radiomic signature. Furthermore, a novel radiomic nomogram was developed for clinical application using the radiomic signature and clinical variables from all patients. Results: Four MRI-based features were selected from the 734 radiomic features, including one location feature. Good discriminative performances were achieved in both training (AUC = 0.859, 95% CI = 0.787-0.932) and validation cohorts (AUC = 0.839, 95% CI = 0.761-0.917) for the type of epilepsy. The accuracies were 80.4 and 80.6%, respectively. The radiomic nomogram also allowed for a high degree of discrimination. All models presented favorable calibration curves and decision curve analyses. Conclusion: Our results suggested that the MRI-based radiomic analysis may predict the type of LGG-related epilepsy to enable individualized therapy for patients with LGG-related epilepsy.
关键词	epilepsy type low-grade gliomas machine learning radiomics T2-weighted imaging
DOI	10.3389/fonc.2020.00235
关键词[WOS]	SEIZURE CHARACTERISTICS ; INTERNATIONAL-LEAGUE ; NERVOUS-SYSTEM ; BRAIN-TUMORS ; CLASSIFICATION ; PROGNOSIS ; SIGNATURE ; DIAGNOSIS ; BIOMARKER ; RESECTION
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[61231004] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2016YFA0100900] ; National Key R&D Program of China[2016YFA0100902] ; Beijing Municipal Natural Science Foundation[7182109] ; Youth Innovation Promotion Association CAS[2019136]
项目资助者	National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Municipal Natural Science Foundation ; Youth Innovation Promotion Association CAS
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000525638400001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：19[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/173211/38810
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.Capital Med Univ, Beijing Tiantan Hosp, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 3.Xian Polytech Univ, Sch Elect & Informat, Xian, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 5.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 7.Capital Med Univ, Beijing Neurosurg Inst, Dept Mol Pathol, Beijing, Peoples R China 8.Beijing Inst Brain Disorders, Ctr Brain Tumor, Beijing, Peoples R China 9.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wang, Yinyan,Wei, Wei,Liu, Zhenyu,et al. Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study[J]. FRONTIERS IN ONCOLOGY,2020,10:8.
APA	Wang, Yinyan.,Wei, Wei.,Liu, Zhenyu.,Liang, Yuchao.,Liu, Xing.,...&Tian, Jie.(2020).Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study.FRONTIERS IN ONCOLOGY,10,8.
MLA	Wang, Yinyan,et al."Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study".FRONTIERS IN ONCOLOGY 10(2020):8.