A deep learning radiomics model for preoperative grading in meningioma.

Autor: Zhu Y; School of Automation, Harbin University of Science and Technology, Heilongjiang, Harbin, 150080, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China., Man C; School of Automation, Harbin University of Science and Technology, Heilongjiang, Harbin, 150080, China., Gong L; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110169, China., Dong D; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China., Yu X; Imaging Center, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214000, China., Wang S; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China., Fang M; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China., Wang S; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100080, China., Fang X; Imaging Center, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214000, China. Electronic address: drfxm@163.com., Chen X; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China. Electronic address: radiology888@aliyun.com., Tian J; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110169, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710126, China. Electronic address: tian@ieee.org.
Jazyk: angličtina
Zdroj: European journal of radiology [Eur J Radiol] 2019 Jul; Vol. 116, pp. 128-134. Date of Electronic Publication: 2019 May 01.
DOI: 10.1016/j.ejrad.2019.04.022
Abstrakt: 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.
(Copyright © 2019. Published by Elsevier B.V.)
Databáze: MEDLINE
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