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 journalArticlepeer-review

9 Citations (Scopus)

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

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2020, European Society of Radiology.

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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