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

Yoon Seong Choi, Sung Soo Ahn, Jong Hee Chang, Seok Gu Kang, Eui Hyun Kim, Se Hoon Kim, Rajan Jain, Seung Koo Lee

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)3834-3842
Number of pages9
JournalEuropean Radiology
Volume30
Issue number7
DOIs
Publication statusPublished - 2020 Jul 1

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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    Choi, Y. S., Ahn, S. S., Chang, J. H., Kang, S. G., Kim, E. H., Kim, S. H., Jain, R., & Lee, S. K. (2020). Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction. European Radiology, 30(7), 3834-3842. https://doi.org/10.1007/s00330-020-06737-5