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

doi:10.1016/j.ebiom.2020.102933

	A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study
	Zhang, Jing 1,2,3,4; Yao, Kuan 4,5; Liu, Panpan 6,7; Liu, Zhenyu4,8,9 ; Han, Tao 1,2,3; Zhao, Zhiyong 1,2; Cao, Yuntai 1,2,3; Zhang, Guojin 1,2,3; Zhang, Junting 6; Tian, Jie 4,8,9,10,11,12; Zhou, Junlin 1,2,3
发表期刊	EBIOMEDICINE
ISSN	2352-3964
	2020-08-01
卷号	58 页码:11
通讯作者	Zhang, Junting(zhangjunting2003@aliyun.com) ; Tian, Jie(jie.tian@ia.ac.cn) ; Zhou, Junlin(ery_zhoujl@lzu.edu.cn)
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/)
关键词	Meningioma Brain invasion Radiomics Magnetic resonance images
DOI	10.1016/j.ebiom.2020.102933
关键词[WOS]	HEALTH-ORGANIZATION CLASSIFICATION ; CENTRAL-NERVOUS-SYSTEM ; DIAGNOSIS ; TUMOR
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; Lanzhou University Second Hospital
WOS研究方向	General & Internal Medicine ; Research & Experimental Medicine
WOS类目	Medicine, General & Internal ; Medicine, Research & Experimental
WOS记录号	WOS:000564188600013
出版者	ELSEVIER
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：55[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/173211/41527
专题	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.Lanzhou Univ, Dept Radiol, Hosp 2, Cuiyingmen 82, Lanzhou 730030, Peoples R China 2.Lanzhou Univ, Second Clin Sch, Lanzhou, Peoples R China 3.Key Lab Med Imaging Gansu Prov, Lanzhou, Peoples R China 4.Chinese Acad Sci, Beijing Key Lab Mol Imaging, Inst Automat, CAS Key Lab Mol Imaging,State Key Lab Management, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 5.Shanghai Jiao Tong Univ, Sch Biomed Engn, Shanghai, Peoples R China 6.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Nansihuan Xilu 119, Beijing, Peoples R China 7.Municipal Hosp Weihai, Dept Neurosurg, Weihai, Peoples R China 8.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 9.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China 10.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China 11.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian 710126, Shaanxi, Peoples R China 12.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big DataBased Precis Med, Beijing 100191, 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	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.