Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images
Joint Authors
Huang, Meiyan
Zhang, Quan
Cao, Jianyun
Zhang, Junde
Bu, Junguo
Yu, Yuwei
Tan, Yujing
Feng, Qianjin
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Purpose.
To classify radiation necrosis versus recurrence in glioma patients using a radiomics model based on combinational features and multimodality MRI images.
Methods.
Fifty-one glioma patients who underwent radiation treatments after surgery were enrolled in this study.
Sixteen patients revealed radiation necrosis while 35 patients showed tumor recurrence during the follow-up period.
After treatment, all patients underwent T1-weighted, T1-weighted postcontrast, T2-weighted, and fluid-attenuated inversion recovery scans.
A total of 41,284 handcrafted and 24,576 deep features were extracted for each patient.
The 0.623 + bootstrap method and the area under the curve (denoted as 0.632 + bootstrap AUC) metric were used to select the features.
The stepwise forward method was applied to construct 10 logistic regression models based on different combinations of image features.
Results.
For handcrafted features on multimodality MRI, model 7 with seven features yielded the highest AUC of 0.9624, sensitivity of 0.8497, and specificity of 0.9083 in the validation set.
These values were higher than the accuracy of using handcrafted features on single-modality MRI (paired t-test, p<0.05, except sensitivity).
For combined handcrafted and AlexNet features on multimodality MRI, model 6 with six features achieved the highest AUC of 0.9982, sensitivity of 0.9941, and specificity of 0.9755 in the validation set.
These values were higher than the accuracy of using handcrafted features on multimodality MRI (paired t-test, p<0.05).
Conclusions.
Handcrafted and deep features extracted from multimodality MRI images reflecting the heterogeneity of gliomas can provide useful information for glioma necrosis/recurrence classification.
American Psychological Association (APA)
Zhang, Quan& Cao, Jianyun& Zhang, Junde& Bu, Junguo& Yu, Yuwei& Tan, Yujing…[et al.]. 2019. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1130509
Modern Language Association (MLA)
Zhang, Quan…[et al.]. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1130509
American Medical Association (AMA)
Zhang, Quan& Cao, Jianyun& Zhang, Junde& Bu, Junguo& Yu, Yuwei& Tan, Yujing…[et al.]. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1130509
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130509