CASIA OpenIR
Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study
Wang, Yinyan1; Wei, Wei2,3,4,5; Liu, Zhenyu2,6; Liang, Yuchao1; Liu, Xing7; Li, Yiming1,7; Tang, Zhenchao2,4; Jiang, Tao1,7,8,9; Tian, Jie2,4,5,6
Source PublicationFRONTIERS IN ONCOLOGY
ISSN2234-943X
2020-03-13
Volume10Pages:8
Corresponding AuthorWang, Yinyan(tiantanyinyan@126.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractPurpose: 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.
Keywordepilepsy type low-grade gliomas machine learning radiomics T2-weighted imaging
DOI10.3389/fonc.2020.00235
WOS KeywordSEIZURE CHARACTERISTICS ; INTERNATIONAL-LEAGUE ; NERVOUS-SYSTEM ; BRAIN-TUMORS ; CLASSIFICATION ; PROGNOSIS ; SIGNATURE ; DIAGNOSIS ; BIOMARKER ; RESECTION
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Municipal Natural Science Foundation ; Youth Innovation Promotion Association CAS
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000525638400001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38810
Collection中国科学院自动化研究所
Corresponding AuthorWang, Yinyan; Tian, Jie
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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