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Radiomic profiles in diffuse glioma reveal distinct subtypes with prognostic value

  • Original Article – Clinical Oncology
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Abstract

Purpose

To evaluate a radiomic approach for the stratification of diffuse gliomas with distinct prognosis and provide additional resolution of their clinicopathological and molecular characteristics.

Methods

For this retrospective study, a total of 704 radiomic features were extracted from the multi-channel MRI data of 166 diffuse gliomas. Survival-associated radiomic features were identified and submitted to distinguish glioma subtypes using consensus clustering. Multi-layered molecular data were used to observe the different clinical and molecular characteristics between radiomic subtypes. The relative profiles of an array of immune cell infiltrations were measured gene set variation analysis approach to explore differences in tumor immune microenvironment.

Results

A total of 6 categories, including 318 radiomic features were significantly correlated with the overall survival of glioma patients. Two subgroups with distinct prognosis were separated by consensus clustering of radiomic features that significantly associated with survival. Histological stage and molecular factors, including IDH status and MGMT promoter methylation status were significant differences between the two subtypes. Furthermore, gene functional enrichment analysis and immune infiltration pattern analysis also hinted that the inferior prognosis subtype may more response to immunotherapy.

Conclusion

A radiomic model derived from multi-parameter MRI of the gliomas was successful in the risk stratification of diffuse glioma patients. These data suggested that radiomics provided an alternative approach for survival estimation and may improve clinical decision-making.

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Acknowledgements

The results shown here are parts based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga and TCIA database: https://www.cancerimagingarchive.net/. Special thanks to the authors (Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin S. Kirby, John B. Freymann, Keyvan Farahani & Christos Davatzikos) of “Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features”. The public online available data are valuable for related research work.

Funding

This research was supported by Guangxi Degree and Postgraduate Education Reform and Development Research Projects, China (JGY2019050), Guangxi Zhuang Autonomous Region Health and Family Planning Commission Self-financed Scientific Research Project (Z20180979).

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Authors and Affiliations

Authors

Contributions

Conception and design: HY and GC. Development of methodology: PL, YTP, and RZG. Analysis and interpretation of data (e.g., statistical analysis, biostatistics, and computational analysis): YW, XJL, SNH, YYF, and ZXW. Writing, review, and/or revision of the manuscript: PL, ZGH, HY, and GC. Study supervision: HY and GC.

Corresponding author

Correspondence to Gang Chen.

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The authors declare that they have no conflict of interest.

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Lin, P., Peng, Yt., Gao, Rz. et al. Radiomic profiles in diffuse glioma reveal distinct subtypes with prognostic value. J Cancer Res Clin Oncol 146, 1253–1262 (2020). https://doi.org/10.1007/s00432-020-03153-6

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  • DOI: https://doi.org/10.1007/s00432-020-03153-6

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