Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

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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Correspondence to Sung Soo Ahn.

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Guarantor

The scientific guarantor of this publication is Sung Soo Ahn.

Conflict of interest

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

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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Keywords

  • Glioma
  • Machine learning
  • Prognosis
  • Survival