CASIA OpenIR
A deep learning radiomics model for preoperative grading in meningioma
Zhu, Yongbei1,2,3; Man, Chuntao1; Gong, Lixin4; Dong, Di2,5; Yu, Xinyi6; Wang, Shuo2,5; Fang, Mengjie2,5; Wang, Siwen2,5; Fang, Xiangming6; Chen, Xuzhu7; Tian, Jie2,3,4,8
Source PublicationEUROPEAN JOURNAL OF RADIOLOGY
ISSN0720-048X
2019-07-01
Volume116Pages:128-134
Corresponding AuthorFang, Xiangming(drfxm@163.com) ; Chen, Xuzhu(radiology888@aliyun.com) ; Tian, Jie(tian@ieee.org)
AbstractObjectives: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI. Methods: We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep learning features were extracted by the convolutional neural network. The random forest algorithm was used to select features with importance values over 0.001, upon which a deep learning signature was built by a linear discriminant analysis classifier. The performance of our DLR model was assessed by discrimination and calibration in the independent validation cohort. For comparison, a radiomic model based on hand-crafted features and a fusion model were built. Results: The DLR signature comprised 39 deep learning features and showed good discrimination performance in both the primary and validation cohorts. The area under curve (AUC), sensitivity, and specificity for predicting meningioma grades were 0.811(95% CI, 0.635-0.986), 0.769, and 0.898 respectively in the validation cohort. DLR performance was superior over the hand-crafted features. Calibration curves of DLR model showed good agreements between the prediction probability and the observed outcome of high-grade meningioma. Conclusions: Using routine MRI data, we developed a DLR model with good performance for noninvasively individualized prediction of meningioma grades, which achieved a quantization capability superior over the hand-crafted features. This model has potential to guide and facilitate the clinical decision-making of whether to observe or to treat patients by providing prognostic information.
KeywordRadiomics Deep learning Meningioma Tumor grading Magnetic resonance imaging
DOI10.1016/j.ejrad.2019.04.022
WOS KeywordCENTRAL-NERVOUS-SYSTEM ; CLASSIFICATION ; SEGMENTATION ; TUMORS ; MRI
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFC0114300] ; National Key R&D Program of China[2018YFC0115604] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81501616] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81671854] ; National Natural Science Foundation of China[81772005] ; National Natural Science Foundation of China[81271629] ; Beijing Natural Science Foundation[L182061] ; Bureau of International Cooperation of Chinese Academy of Sciences[173211KYSB20160053] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201502] ; Youth Innovation Promotion Association CAS[2017175] ; Natural Science Foundation of Heilongjiang Province[F201216]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Bureau of International Cooperation of Chinese Academy of Sciences ; Instrument Developing Project of the Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Natural Science Foundation of Heilongjiang Province
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000469325700018
PublisherELSEVIER IRELAND LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24381
Collection中国科学院自动化研究所
Corresponding AuthorFang, Xiangming; Chen, Xuzhu; Tian, Jie
Affiliation1.Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
4.Northeastern Univ, Sino Dutch Biomed & Informat Engn Sch, Shenyang 110169, Liaoning, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
6.Nanjing Med Univ, Wuxi Peoples Hosp, Imaging Ctr, 299 Qingyang Rd, Wuxi 214000, Jiangsu, Peoples R China
7.Capital Med Univ, Beijing Tiantan Hosp, Dept Radiol, 119 Nansihuan Xilu, Beijing 100050, Peoples R China
8.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian 710126, Shaanxi, 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
Zhu, Yongbei,Man, Chuntao,Gong, Lixin,et al. A deep learning radiomics model for preoperative grading in meningioma[J]. EUROPEAN JOURNAL OF RADIOLOGY,2019,116:128-134.
APA Zhu, Yongbei.,Man, Chuntao.,Gong, Lixin.,Dong, Di.,Yu, Xinyi.,...&Tian, Jie.(2019).A deep learning radiomics model for preoperative grading in meningioma.EUROPEAN JOURNAL OF RADIOLOGY,116,128-134.
MLA Zhu, Yongbei,et al."A deep learning radiomics model for preoperative grading in meningioma".EUROPEAN JOURNAL OF RADIOLOGY 116(2019):128-134.
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