CASIA OpenIR
Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma
Li, Longfei1,2; Wang, Ke3,4; Ma, Xiujian3,4; Liu, Zhenyu2,5; Wang, Shuo2; Du, Jiang6; Tian, Kaibing3,4; Zhou, Xuezhi7; Wei, Wei7; Sun, Kai7; Lin, Yusong8; Wu, Zhen3,4; Tian, Jie2,5,7,9
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-09-01
Volume118Pages:81-87
Corresponding AuthorLin, Yusong(yslin@ha.edu.cn) ; Wu, Zhen(wuzhen1966@aliyun.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractPurpose: Patients with skull base chordoma and chondrosarcoma have different prognoses and are not readily differentiated preoperatively on imaging. Multiparametric magnetic resonance imaging (MRI) is a routine diagnostic tool that can noninvasively characterize the salient characteristics of tumors. In the present study, we developed and validated a preoperative multiparametric MRI-based radiomic signature for differentiating these tumors. Method: This retrospective study enrolled 210 patients and consecutively divided them into the primary and validation cohorts. A total of 1941 radiomic features were acquired from preoperative T1-weighted imaging, T2-weighted imaging and contrast-enhanced Tl-weighted imaging for each patient. The most discriminative features were selected by minimum-redundancy maximum-relevancy and recursive feature elimination algorithms in the primary cohort. The multiparametric and single-sequence MRI signatures were constructed with the selected features using a support vector machine model in the primary cohort. The ability of the novel radiomic signatures to differentiate chordoma from chondrosarcoma were assessed using receiver operating characteristic curve analysis in the validation cohort. Results: The multiparametric radiomic signature, which consisted of 11 selected features, reached an area under the receiver operating characteristic curve of 0.9745 and 0.8720 in the primary and validation cohorts, respectively. Moreover, compared with each single-sequence MRI signature, the multiparametric radiomic signature exhibited better classification performance with significant improvement (p < 0.05, Delong's test) in the primary cohorts. Conclusion: By combining features from three MRI sequences, the multiparametric radiomics signature can accurately and robustly differentiate skull base chordoma from chondrosarcoma.
KeywordRadiomics Chordoma Chondrosarcoma Multiparametric MRI
DOI10.1016/j.ejrad.2019.07.006
WOS KeywordCLASSIFICATION ; INFORMATION ; SELECTION ; FEATURES ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81772009] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81772012] ; National Natural Science Foundation of China[81672506] ; National Natural Science Foundation of China[81802683] ; National Key Research and Development Plan of China[2017YFA0205200] ; Scientific and Technological Research Project of Henan Province[182102310162] ; Youth Innovation Promotion Association CAS[2019136] ; Beijing Natural Science Foundation[7182109] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Chinese Academy of Sciences
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Plan of China ; Scientific and Technological Research Project of Henan Province ; Youth Innovation Promotion Association CAS ; Beijing Natural Science Foundation ; Beijing Municipal Science & Technology Commission ; Chinese Academy of Sciences
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000481609300013
PublisherELSEVIER IRELAND LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27560
Collection中国科学院自动化研究所
Corresponding AuthorLin, Yusong; Wu, Zhen; Tian, Jie
Affiliation1.Zhengzhou Univ, Collaborat Innovat Ctr Internet Healthcare, 75 Daxue Rd, Zhengzhou 450052, Henan, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Capital Med Univ, Beijing Tian Tan Hosp, Dept Neurosurg, Beijing, Peoples R China
4.China Natl Clin Res Ctr Neurol Dis, Beijing 100050, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
6.Beijing Neurosurg Inst, Dept Neuropathol, Beijing 100050, Peoples R China
7.Xidian Univ, Sch Life Sci & Technol, Xian 710071, Shaanxi, Peoples R China
8.Zhengzhou Univ, Sch Software, Zhengzhou 450003, Henan, Peoples R China
9.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Longfei,Wang, Ke,Ma, Xiujian,et al. Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,118:81-87.
APA Li, Longfei.,Wang, Ke.,Ma, Xiujian.,Liu, Zhenyu.,Wang, Shuo.,...&Tian, Jie.(2019).Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma.EUROPEAN JOURNAL OF RADIOLOGY,118,81-87.
MLA Li, Longfei,et al."Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma".EUROPEAN JOURNAL OF RADIOLOGY 118(2019):81-87.
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