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A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

文献类型：期刊论文

作者Zhang, Jing4,5,6,12; Yao, Kuan2,12; Liu, Panpan7,8; Liu, Zhenyu9,10,12; Han, Tao4,5,6; Zhao, Zhiyong5,6; Cao, Yuntai4,5,6; Zhang, Guojin4,5,6; Zhang, Junting7; Tian, Jie1,3,9,10,11,12
刊名EBIOMEDICINE
出版日期2020-08-01
卷号58页码:11
关键词Meningioma Brain invasion Radiomics Magnetic resonance images
ISSN号2352-3964
DOI10.1016/j.ebiom.2020.102933
通讯作者Zhang, Junting(zhangjunting2003@aliyun.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn)
英文摘要Background: Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features. Methods: In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Findings: Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0.857 (95% CI, 0.831-0.887) and 0.819 (95% CI, 0.775-0.863) and sensitivities of 72.8% and 90.1% in the training and validation cohorts, respectively. Interpretation: Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
WOS关键词HEALTH-ORGANIZATION CLASSIFICATION ; CENTRAL-NERVOUS-SYSTEM ; DIAGNOSIS ; TUMOR
资助项目National Natural Science Foundation of China[81772006] ; National Natural Science Foundation of China[81922040] ; Youth Innovation Promotion Association CAS[2019136] ; Lanzhou University Second Hospital[YJS-BD-33]
WOS研究方向General & Internal Medicine ; Research & Experimental Medicine
语种英语
WOS记录号WOS:000564188600013
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; Lanzhou University Second Hospital
源URL[http://ir.ia.ac.cn/handle/173211/41526]  
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Zhang, Junting; Tian, Jie; Zhou, Junlin
作者单位1.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian 710126, Shaanxi, Peoples R China
2.Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China
3.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big DataBased Precis Med, Beijing 100191, Peoples R China
4.Key Lab Med Imaging Gansu Prov, Lanzhou, Peoples R China
5.Lanzhou Univ, Second Clin Sch, Lanzhou, Peoples R China
6.Lanzhou Univ, Dept Radiol, Hosp 2, Cuiyingmen 82, Lanzhou 730030, Peoples R China
7.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Nansihuan Xilu 119, Beijing, Peoples R China
8.Municipal Hosp Weihai, Dept Neurosurg, Weihai, Peoples R China
9.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
10.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jing,Yao, Kuan,Liu, Panpan,et al. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study[J]. EBIOMEDICINE,2020,58:11.
APA Zhang, Jing.,Yao, Kuan.,Liu, Panpan.,Liu, Zhenyu.,Han, Tao.,...&Zhou, Junlin.(2020).A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study.EBIOMEDICINE,58,11.
MLA Zhang, Jing,et al."A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study".EBIOMEDICINE 58(2020):11.

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来源：自动化研究所

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