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Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status

Authors
 Chae Jung Park  ;  Yoon Seong Choi  ;  Yae Won Park  ;  Sung Soo Ahn  ;  Seok-Gu Kang  ;  Jong-Hee Chang  ;  Se Hoon Kim  ;  Seung-Koo Lee 
Citation
 NEURORADIOLOGY, Vol.62(3) : 319-326, 2020 
Journal Title
 NEURORADIOLOGY 
ISSN
 0028-3940 
Issue Date
2020
Keywords
Diffusion tensor imaging ; Isocitrate dehydrogenase ; Lower-grade glioma ; Machine learning ; Radiomics
Abstract
PURPOSE: To evaluate whether diffusion tensor imaging (DTI) radiomics with machine learning improves the prediction of isocitrate dehydrogenase (IDH) mutation status of lower-grade gliomas beyond radiomic features from conventional MRI and DTI histogram parameters. METHODS: A total of 168 patients with pathologically confirmed lower-grade gliomas were retrospectively enrolled. A total of 158 and 253 radiomic features were extracted from DTI (DTI radiomics) and conventional MRI (T1-weighted image with contrast enhancement, T2-weighted image, and FLAIR [conventional radiomics]), respectively. The random forest models for predicting IDH status were trained with variable combinations as follows: (1) DTI radiomics, (2) conventional radiomics, (3) conventional radiomics + DTI radiomics, and (4) conventional radiomics + DTI histogram. The models were validated with nested cross-validation. The predictive performances of those models were compared by using area under the curve (AUC) from receiver operating characteristic analysis, and 95% confidence interval (CI) was calculated. RESULTS: Adding DTI radiomics to conventional radiomics significantly improved the accuracy of IDH status subtyping (AUC, 0.900 [95% CI, 0.855-0.945], p = 0.006), whereas adding DTI histogram parameters yielded nonsignificant trend toward improvement (0.869 [95% CI, 0.816-0.922], p = 0.150) compared with the model with conventional radiomics alone (0.835 [95% CI, 0.773-0.896]). The performance of the model consisting of both DTI and conventional radiomics was significantly superior than that of model consisting of both DTI histogram parameters and conventional radiomics (0.900 vs 0.869, p = 0.040). CONCLUSION: DTI radiomics with machine learning can help improve the subtyping of IDH status beyond conventional radiomics and DTI histogram parameters in patients with lower-grade gliomas.
Full Text
https://link.springer.com/article/10.1007%2Fs00234-019-02312-y
DOI
10.1007/s00234-019-02312-y
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 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
Park, Chae Jung(박채정) ORCID logo https://orcid.org/0000-0002-5567-8658
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/22282913/175550
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