Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour

doi:10.1007/s00330-018-5704-8

	Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour
	Su, Changliang 1; Jiang, Jingjing 1; Zhang, Shun 1; Shi, Jingjing 1; Xu, Kaibin2,3,4 ; Shen, Nanxi 1; Zhang, Jiaxuan 1; Li, Li 1; Zhao, Lingyun 1; Zhang, Ju 1; Qin, Yuanyuan 1; Liu, Yong2,3,4,5 ; Zhu, Wenzhen 1
发表期刊	EUROPEAN RADIOLOGY
ISSN	0938-7994
	2019-04-01
卷号	29 期号:4 页码:1986-1996
通讯作者	Liu, Yong(yliu@nlpr.ia.ac.cn) ; Zhu, Wenzhen(zhuwenzhen8612@163.com)
摘要	PurposeTo explore the feasibility and diagnostic performance of radiomics based on anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and predicting tumour proliferation.Methods220 pathology-confirmed gliomas and ten contrasts were included in the retrospective analysis. After being registered to T2FLAIR images and resampling to 1 mm(3) isotropically, 431 radiomics features were extracted from each contrast map within a semi-automatic defined tumour volume. For single-contrast and the combination of all contrasts, correlations between the radiomics features and pathological biomarkers were revealed by partial correlation analysis, and multivariate models were built to identify the best predictive models with adjusted 0.632+ bootstrap AUC.ResultsIn univariate analysis, both non-wavelet and wavelet radiomics features were correlated significantly with tumour grade and the Ki-67 labelling index. The max R was 0.557 (p=2.04E-14) in T1C for tumour grade and 0.395 (p=2.33E-07) in ADC for Ki-67. In the multivariate analysis, the combination of all-contrast radiomics features had the highest AUCs in both differentiating among glioma subtypes and predicting proliferation compared with those in single-contrast images. For low-/high-grade gliomas, the best AUC was 0.911. In differentiating among glioma subtypes, the best AUC was 0.896 for grades II-III, 0.997 for grades II-IV, and 0.881 for grades III-IV. In predicting proliferation levels, multicontrast features led to an AUC of 0.936.ConclusionMulticontrast radiomics supplies complementary information on both geometric characters and molecular biological traits, which correlated significantly with tumour grade and proliferation. Combining all-contrast radiomics models might precisely predict glioma biological behaviour, which may be attributed to presurgical personal diagnosis.Key Points center dot Multicontrast MRI radiomics features are significantly correlated with tumour grade and Ki-67 LI.center dot Multimodality MRI provides independent but supplemental information in assessing glioma pathological behaviour.center dot Combined multicontrast MRI radiomics can precisely predict glioma subtypes and proliferation levels.
关键词	Radiomics Glioma Neoplasm grading Cell proliferation Magnetic resonance imaging
DOI	10.1007/s00330-018-5704-8
关键词[WOS]	MONOCLONAL-ANTIBODY ; BRAIN-TUMORS ; GRADE ; FEATURES ; IMAGES
收录类别	SCI
语种	英语
资助项目	National Program of the Ministry of Science and Technology of China[2011BAI08B10] ; National Natural Science Foundation of China[81171308] ; National Natural Science Foundation of China[81570462] ; National Natural Science Foundation of China[81730049] ; National Program of the Ministry of Science and Technology of China[2011BAI08B10] ; National Natural Science Foundation of China[81171308] ; National Natural Science Foundation of China[81570462] ; National Natural Science Foundation of China[81730049]
项目资助者	National Program of the Ministry of Science and Technology of China ; National Natural Science Foundation of China
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000461329400041
出版者	SPRINGER
七大方向——子方向分类	人工智能+医疗
引用统计	被引频次：68[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/173211/28080
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Liu, Yong; Zhu, Wenzhen
作者单位	1.Huazhong Univ Sci & Technol, Tongji Med Coll, Tongji Hosp, Dept Radiol, 1095 JieFang Ave, Wuhan, Hubei, Peoples R China 2.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China 3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
通讯作者单位	模式识别国家重点实验室; 中国科学院自动化研究所
推荐引用方式 GB/T 7714	Su, Changliang,Jiang, Jingjing,Zhang, Shun,et al. Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour[J]. EUROPEAN RADIOLOGY,2019,29(4):1986-1996.
APA	Su, Changliang.,Jiang, Jingjing.,Zhang, Shun.,Shi, Jingjing.,Xu, Kaibin.,...&Zhu, Wenzhen.(2019).Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.EUROPEAN RADIOLOGY,29(4),1986-1996.
MLA	Su, Changliang,et al."Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour".EUROPEAN RADIOLOGY 29.4(2019):1986-1996.