CASIA OpenIR
Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas
Han, Yuqi1,2,4; Xie, Zhen3; Zang, Yali2,4,8; Zhang, Shuaitong2,4,8; Gu, Dongsheng2,4,8; Zhou, Mu5; Gevaert, Olivier5; Wei, Jingwei2,4,8; Li, Chao3; Chen, Hongyan6; Du, Jiang6; Liu, Zhenyu2,4,8; Dong, Di2,4,8; Tian, Jie2,4,7,8; Zhou, Dabiao3,9
Source PublicationJOURNAL OF NEURO-ONCOLOGY
ISSN0167-594X
2018-11-01
Volume140Issue:2Pages:297-306
Corresponding AuthorDong, Di(di.dong@ia.ac.cn) ; Tian, Jie(tian@ieee.org) ; Zhou, Dabiao(dabiaozhou@163.com)
AbstractPurposeTo perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.MethodsThis retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n=184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n=93).ResultsThe radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction.ConclusionOur study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.
KeywordLower-grade glioma 1p 19q Co-deletion Prediction Radiomics Magnetic resonance imaging
DOI10.1007/s11060-018-2953-y
WOS KeywordOLIGODENDROGLIAL TUMORS ; GLIOBLASTOMA-MULTIFORME ; SURVIVAL ; 1P ; CLASSIFICATION ; RADIOTHERAPY ; TEMOZOLOMIDE ; MANAGEMENT ; IMPACT ; 19Q
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81771924] ; National Key Research and Development Program of China[2106YFC0103702] ; National Key Research and Development Program of China[2017YFA0205200] ; National Institute of Biomedical Imaging And Bioengineering of the National Institutes of Health[R01EB020527]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China ; National Institute of Biomedical Imaging And Bioengineering of the National Institutes of Health
WOS Research AreaOncology ; Neurosciences & Neurology
WOS SubjectOncology ; Clinical Neurology
WOS IDWOS:000450472400011
PublisherSPRINGER
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25722
Collection中国科学院自动化研究所
Corresponding AuthorDong, Di; Tian, Jie; Zhou, Dabiao
Affiliation1.Xidian Univ, Sch Life Sci & Technol, Xian 710126, Shaanxi, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing 100050, Peoples R China
4.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Stanford Univ, Stanford Ctr Biomed Informat Res, Palo Alto, CA 94304 USA
6.Capital Med Univ, Beijing Neurosurg Inst, Dept Neuroradiol, 6 Tiantanxili, Beijing 100050, Peoples R China
7.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
9.China Natl Clin Res Ctr Neurol Dis, Beijing 100050, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China;  Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Han, Yuqi,Xie, Zhen,Zang, Yali,et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas[J]. JOURNAL OF NEURO-ONCOLOGY,2018,140(2):297-306.
APA Han, Yuqi.,Xie, Zhen.,Zang, Yali.,Zhang, Shuaitong.,Gu, Dongsheng.,...&Zhou, Dabiao.(2018).Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas.JOURNAL OF NEURO-ONCOLOGY,140(2),297-306.
MLA Han, Yuqi,et al."Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas".JOURNAL OF NEURO-ONCOLOGY 140.2(2018):297-306.
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