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Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.
- Source :
- European Radiology; Jul2018, Vol. 28 Issue 7, p2960-2968, 9p, 1 Diagram, 2 Charts, 3 Graphs
- Publication Year :
- 2018
-
Abstract
- <bold>Objectives: </bold>To predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis.<bold>Methods: </bold>Cancer Genome Atlas (TCGA) patients with lower-grade gliomas were randomly allocated into training (n = 63) and validation (n = 32) sets. An independent external-validation set (n = 91) was built based on the Chinese Genome Atlas (CGGA) database. After feature extraction, an ATRX-related signature was constructed. Subsequently, the radiomic signature was combined with a support vector machine to predict ATRX mutation status in training, validation and external-validation sets. Predictive performance was assessed by receiver operating characteristic curve analysis. Correlations between the selected features were also evaluated.<bold>Results: </bold>Nine radiomic features were screened as an ATRX-associated radiomic signature of lower-grade gliomas based on the LASSO regression model. All nine radiomic features were texture-associated (e.g. sum average and variance). The predictive efficiencies measured by the area under the curve were 94.0 %, 92.5 % and 72.5 % in the training, validation and external-validation sets, respectively. The overall correlations between the nine radiomic features were low in both TCGA and CGGA databases.<bold>Conclusions: </bold>Using radiomic analysis, we achieved efficient prediction of ATRX genotype in lower-grade gliomas, and our model was effective in two independent databases.<bold>Key Points: </bold>• ATRX in lower-grade gliomas could be predicted using radiomic analysis. • The LASSO regression algorithm and SVM performed well in radiomic analysis. • Nine radiomic features were screened as an ATRX-predictive radiomic signature. • The machine-learning model for ATRX-prediction was validated by an independent database. [ABSTRACT FROM AUTHOR]
- Subjects :
- GENOTYPES
GLIOMAS
MAGNETIC resonance imaging
MUTATION statistics
REGRESSION analysis
Subjects
Details
- Language :
- English
- ISSN :
- 09387994
- Volume :
- 28
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- European Radiology
- Publication Type :
- Academic Journal
- Accession number :
- 129951949
- Full Text :
- https://doi.org/10.1007/s00330-017-5267-0