Abstract
Background and purpose
Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status.
Materials and methods
Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics.
Results
The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501–0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003–0.209).
Conclusion
Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas.
Key Points
• Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.
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Abbreviations
- FLAIR:
-
Fluid-attenuated inversion recovery
- iAUC:
-
Integrated area under the ROC curve
- IDH:
-
Isocitrate dehydrogenase
- IRB:
-
Institutional research board
- LGG:
-
Lower grade glioma
- OS:
-
Overall survival
- RF:
-
Random forest
- ROC:
-
Receiver operating characteristics
- RSF:
-
Random survival forest
- T1C:
-
T1-weighted contrast-enhanced
- T2:
-
T2-weighted
- TCGA:
-
The Cancer Genome Atlas
- TCIA:
-
The Cancer Imaging Archive
- TE:
-
Echo time
- TR:
-
Repetition time
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Funding
This research received funding from the Basic Science Research Program through the National Research Foundation of Korea which is funded by the Ministry of Science, ICT & Future Planning (2017R1D1A1B03030440). This study was also supported by a faculty research grant from the Yonsei University College of Medicine (6-2016-0121) and by DongKook Life Science. Co., Ltd., Republic of Korea.
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The scientific guarantor of this publication is Sung Soo Ahn.
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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
The statistical methodology of this study was reviewed by Kyunghwa Han, Yonsei University College of Medicine.
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Written informed consent was waived by the Institutional Review Board.
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Institutional Review Board approval was obtained.
Methodology
• Retrospective
• Cross-sectional study
• Multi-center study
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Choi, Y.S., Ahn, S.S., Chang, J.H. et al. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction. Eur Radiol 30, 3834–3842 (2020). https://doi.org/10.1007/s00330-020-06737-5
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DOI: https://doi.org/10.1007/s00330-020-06737-5