CASIA OpenIR
Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour
Su, Changliang1; Jiang, Jingjing1; Zhang, Shun1; Shi, Jingjing1; Xu, Kaibin2,3,4; Shen, Nanxi1; Zhang, Jiaxuan1; Li, Li1; Zhao, Lingyun1; Zhang, Ju1; Qin, Yuanyuan1; Liu, Yong2,3,4,5; Zhu, Wenzhen1
Source PublicationEUROPEAN RADIOLOGY
ISSN0938-7994
2019-04-01
Volume29Issue:4Pages:1986-1996
Corresponding AuthorLiu, Yong(yliu@nlpr.ia.ac.cn) ; Zhu, Wenzhen(zhuwenzhen8612@163.com)
AbstractPurposeTo 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.
KeywordRadiomics Glioma Neoplasm grading Cell proliferation Magnetic resonance imaging
DOI10.1007/s00330-018-5704-8
WOS KeywordMONOCLONAL-ANTIBODY ; BRAIN-TUMORS ; GRADE ; FEATURES ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Program of the Ministry of Science and Technology of China ; National Natural Science Foundation of China
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000461329400041
PublisherSPRINGER
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28080
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Yong; Zhu, Wenzhen
Affiliation1.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
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
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