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

Authors
 Yoon Seong Choi  ;  Sung Soo Ahn  ;  Jong Hee Chang  ;  Seok-Gu Kang  ;  Eui Hyun Kim  ;  Se Hoon Kim  ;  Rajan Jain  ;  Seung-Koo Lee 
Citation
 EUROPEAN RADIOLOGY, Vol.30(7) : 3834-3842, 2020-07 
Journal Title
 EUROPEAN RADIOLOGY 
ISSN
 0938-7994 
Issue Date
2020-07
Keywords
Glioma ; Machine learning ; Prognosis ; Survival
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.
Full Text
https://link.springer.com/article/10.1007%2Fs00330-020-06737-5
DOI
10.1007/s00330-020-06737-5
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kang, Seok Gu(강석구) ORCID logo https://orcid.org/0000-0001-5676-2037
Kim, Se Hoon(김세훈) ORCID logo https://orcid.org/0000-0001-7516-7372
Kim, Eui Hyun(김의현) ORCID logo https://orcid.org/0000-0002-2523-7122
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Chang, Jong Hee(장종희) ORCID logo https://orcid.org/0000-0003-1509-9800
Choi, Yoon Seong(최윤성)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179555
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