CASIA OpenIR
Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis
Ma, Xiaoxiao1,2; Zhang, Liwen2,3; Huang, Dehui4; Lyu, Jinhao1; Fang, Mengjie2; Hu, Jianxing1; Zang, Yali2; Zhang, Dekang1; Shao, Hang5; Ma, Lin1; Tian, Jie2; Dong, Di2; Lou, Xin1
Source PublicationJOURNAL OF MAGNETIC RESONANCE IMAGING
ISSN1053-1807
2019-04-01
Volume49Issue:4Pages:1113-1121
Corresponding AuthorTian, Jie(tian@ieee.org) ; Dong, Di(di.dong@ia.ac.cn) ; Lou, Xin(louxin@301hospital.com.cn)
AbstractBackground Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. Purpose To investigate radiomic signatures as potential imaging biomarkers for distinguishing NMOSD from MS, and to develop and validate a diagnostic radiomic-signature-based nomogram for individualized disease discrimination. Study Type Retrospective, cross-sectional study. Subjects Seventy-seven NMOSD patients and 73 MS patients. Field Strength/Sequence 3T/T-2-weighted imaging. Assessment Eighty-eight patients and 62 patients were respectively enrolled in the primary and validation cohorts. Quantitative radiomic features were automatically extracted from lesioned regions on T-2-weighted imaging. A least absolute shrinkage and selection operator analysis was used to reduce the dimensionality of features. Finally, we constructed a radiomic nomogram for disease discrimination. Statistical Tests Features were compared using the Mann-Whitney U-test with a nonnormal distribution. We depicted the nomogram on the basis of the results of the logistic regression using the rms package in R. The Hmisc package was used to investigate the performance of the nomogram via Harrell's C-index. Results A total of 273 quantitative radiomic features were extracted from lesions. A multivariable analysis selected 11 radiomic features and five clinical features to be included in the model. The radiomic signature (P < 0.001 for both the primary and validation cohorts) showed good potential for building a classification model for disease discrimination. The area under the receiver operating characteristic curve was 0.9880 for the training cohort and 0.9363 for the validation cohort. The nomogram exhibited good discrimination, a concordance index of 0.9363, and good calibration in the primary cohort. The nomogram showed similar discrimination, concordance (0.9940), and calibration in the validation cohort. Data Conclusion The diagnostic radiomic-signature-based nomogram has potential utility for individualized disease discrimination of NMOSD from MS in clinical practice.
DOI10.1002/jmri.26287
WOS KeywordGREY-MATTER ; MRI ; NOMOGRAM ; FEATURES ; LESIONS ; MARKER ; IMAGES ; IRON ; NMO ; 7T
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[61231004] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81671126] ; National Natural Science Foundation of China[81730048] ; Special Program for Science and Technology Development from the Ministry of Science and Technology, China[2017YFA0205200] ; Special Program for Science and Technology Development from the Ministry of Science and Technology, China[2017YFC1308701] ; Special Program for Science and Technology Development from the Ministry of Science and Technology, China[2017YFC1309100] ; Special Program for Science and Technology Development from the Ministry of Science and Technology, China[2016CZYD0001] ; Special Program for Science and Technology Development from the Ministry of Science and Technology, China[2016YFC0100104] ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-SW-STS-160]
Funding OrganizationNational Natural Science Foundation of China ; Special Program for Science and Technology Development from the Ministry of Science and Technology, China ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000461233600021
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24975
Collection中国科学院自动化研究所
Corresponding AuthorTian, Jie; Dong, Di; Lou, Xin
Affiliation1.Chinese Peoples Liberat Army Gen Hosp, Dept Radiol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Dept Neurol, Beijing, Peoples R China
5.Tsinghua Univ, Automat Dept, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ma, Xiaoxiao,Zhang, Liwen,Huang, Dehui,et al. Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis[J]. JOURNAL OF MAGNETIC RESONANCE IMAGING,2019,49(4):1113-1121.
APA Ma, Xiaoxiao.,Zhang, Liwen.,Huang, Dehui.,Lyu, Jinhao.,Fang, Mengjie.,...&Lou, Xin.(2019).Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis.JOURNAL OF MAGNETIC RESONANCE IMAGING,49(4),1113-1121.
MLA Ma, Xiaoxiao,et al."Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis".JOURNAL OF MAGNETIC RESONANCE IMAGING 49.4(2019):1113-1121.
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