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

Title
Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status
Authors
Park, Chae JungChoi, Yoon SeongPark, Yae WonAhn, Sung SooKang, Seok-GuChang, Jong-HeeKim, Se HoonLee, Seung-Koo
Ewha Authors
박예원
Issue Date
2020
Journal Title
NEURORADIOLOGY
ISSN
0028-3940JCR Link

1432-1920JCR Link
Citation
NEURORADIOLOGY vol. 62, no. 3, pp. 319 - 326
Keywords
Diffusion tensor imagingIsocitrate dehydrogenaseLower-grade gliomaMachine learningRadiomics
Publisher
SPRINGER
Indexed
SCI; SCIE; SCOPUS WOS scopus
Document Type
Article
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.
DOI
10.1007/s00234-019-02312-y
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의료원 > 의료원 > Journal papers
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