Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | EUROPEAN JOURNAL OF RADIOLOGY |
ISSN | 0720-048X |
2019-07-01 | |
卷号 | 116页码:128-134 |
摘要 | Objectives: 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. |
关键词 | Radiomics Deep learning Meningioma Tumor grading Magnetic resonance imaging |
DOI | 10.1016/j.ejrad.2019.04.022 |
关键词[WOS] | CENTRAL-NERVOUS-SYSTEM ; CLASSIFICATION ; SEGMENTATION ; TUMORS ; MRI |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000469325700018 |
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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/24381 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Fang, Xiangming; Chen, Xuzhu; Tian, Jie |
作者单位 | 1.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 |
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 | 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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