Predicting the Type of Tumor-Related Epilepsy in Patients With Low-Grade Gliomas: A Radiomics Study | |
Wang, Yinyan4; Wei, Wei3,5,8,9; Liu, Zhenyu3,6; Liang, Yuchao4; Liu, Xing1; Li, Yiming1,4; Tang, Zhenchao3,9; Jiang, Tao1,2,4,7; Tian, Jie3,5,6,9 | |
刊名 | FRONTIERS IN ONCOLOGY |
2020-03-13 | |
卷号 | 10页码:8 |
关键词 | epilepsy type low-grade gliomas machine learning radiomics T2-weighted imaging |
ISSN号 | 2234-943X |
DOI | 10.3389/fonc.2020.00235 |
通讯作者 | Wang, Yinyan(tiantanyinyan@126.com) ; Tian, Jie(jie.tian@ia.ac.cn) |
英文摘要 | 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. |
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] |
WOS关键词 | SEIZURE CHARACTERISTICS ; INTERNATIONAL-LEAGUE ; NERVOUS-SYSTEM ; BRAIN-TUMORS ; CLASSIFICATION ; PROGNOSIS ; SIGNATURE ; DIAGNOSIS ; BIOMARKER ; RESECTION |
WOS研究方向 | Oncology |
语种 | 英语 |
出版者 | FRONTIERS MEDIA SA |
WOS记录号 | WOS:000525638400001 |
资助机构 | National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Municipal Natural Science Foundation ; Youth Innovation Promotion Association CAS |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/38809] |
专题 | 自动化研究所_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 Neurosurg Inst, Dept Mol Pathol, Beijing, Peoples R China 2.China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China 4.Capital Med Univ, Beijing Tiantan Hosp, 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.Beijing Inst Brain Disorders, Ctr Brain Tumor, Beijing, Peoples R China 8.Xian Polytech Univ, Sch Elect & Informat, Xian, Peoples R China 9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China |
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. |
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