Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma

doi:10.1016/j.ejrad.2019.07.006

	Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma
	Li, Longfei 1,2; Wang, Ke 3,4; Ma, Xiujian 3,4; Liu, Zhenyu2,5 ; Wang, Shuo2 ; Du, Jiang 6; Tian, Kaibing 3,4; Zhou, Xuezhi 7; Wei, Wei 7; Sun, Kai 7; Lin, Yusong 8; Wu, Zhen 3,4; Tian, Jie 2,5,7,9
发表期刊	EUROPEAN JOURNAL OF RADIOLOGY
ISSN	0720-048X
	2019-09-01
卷号	118 页码:81-87
通讯作者	Lin, Yusong(yslin@ha.edu.cn) ; Wu, Zhen(wuzhen1966@aliyun.com) ; Tian, Jie(jie.tian@ia.ac.cn)
摘要	Purpose: 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.
关键词	Radiomics Chordoma Chondrosarcoma Multiparametric MRI
DOI	10.1016/j.ejrad.2019.07.006
关键词[WOS]	CLASSIFICATION ; INFORMATION ; SELECTION ; FEATURES ; IMAGES
收录类别	SCI
语种	英语
资助项目	National 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 ; National 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
项目资助者	National 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研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000481609300013
出版者	ELSEVIER IRELAND LTD
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：42[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/173211/27560
专题	中国科学院分子影像重点实验室
通讯作者	Lin, Yusong; Wu, Zhen; Tian, Jie
作者单位	1.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
第一作者单位	中国科学院分子影像重点实验室
通讯作者单位	中国科学院分子影像重点实验室
推荐引用方式 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.