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European Journal of Nuclear Medicine and Molecular Imaging
Published in: European Journal of Nuclear Medicine and Molecular Imaging 6/2020

Open Access 01-06-2020 | Glioma | Original Article

Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model

Authors: Kai Wang, Zhen Qiao, Xiaobin Zhao, Xiaotong Li, Xin Wang, Tingfan Wu, Zhongwei Chen, Di Fan, Qian Chen, Lin Ai

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 6/2020

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Abstract

Purpose

To develop and validate an integrated model for discriminating tumor recurrence from radiation necrosis in glioma patients.

Methods

Data from 160 pathologically confirmed glioma patients were analyzed. The diagnostic model was developed in a primary cohort (n = 112). Textural features were extracted from postoperative 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), 11C-methionine (11C-MET) PET, and magnetic resonance images. The least absolute shrinkage and selection operator regression model was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a model for predicting tumor recurrence. The radiomics signature, quantitative PET parameters, and clinical risk factors were incorporated in the model. The clinical value of the model was then assessed in an independent validation cohort using the remaining 48 glioma patients.

Results

The integrated model consisting of 15 selected features was significantly associated with postoperative tumor recurrence (p < 0.001 for both primary and validation cohorts). Predictors contained in the individualized diagnosis model included the radiomics signature, the mean of tumor-background ratio (TBR) of 18F-FDG, maximum of TBR of 11C-MET PET, and patient age. The integrated model demonstrated good discrimination, with an area under the curve (AUC) of 0.988, with a 95% confidence interval (CI) of 0.975–1.000. Application in the validation cohort showed good differentiation (AUC of 0.914 and 95% CI of 0.881–0.945). Decision curve analysis showed that the integrated diagnosis model was clinically useful.

Conclusions

Our developed model could be used to assist the postoperative individualized diagnosis of tumor recurrence in patients with gliomas.
Appendix
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Metadata
Title
Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model
Authors
Kai Wang
Zhen Qiao
Xiaobin Zhao
Xiaotong Li
Xin Wang
Tingfan Wu
Zhongwei Chen
Di Fan
Qian Chen
Lin Ai
Publication date
01-06-2020
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 6/2020
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-019-04604-0

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