Genotype prediction of ATRX mutation in lower-grade gliomas using
an MRI radiomics signature
Received: 9 October 2017 /Revised: 25 November 2017 /Accepted: 20 December 2017 /Published online: 5 February 2018
European Society of Radiology 2018
Objectives To predict ATRX mutation status in patients with lower-grade gliomas using radiomic analysis.
Methods 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 com-
bined 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.
Results 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
Conclusions Using radiomic analysis, we achieved efficient prediction of ATRX genotype in lower-grade gliomas, and our
model was effective in two independent databases.
• 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.
Keywords Magnetic resonance imaging
Yiming Li and Xing Liu contributed equally to this work.
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s00330-017-5267-0) contains supplementary
material, which is available to authorized users.
* Yinyan Wang
* Tao Jiang
Beijing Neurosurgical Institute, Capital Medical University, 6
Tiantanxili, Beijing 100050, China
Chinese Academy of Sciences, Institute of Automation,
Department of Neuroradiology, Beijing Tiantan Hospital, Capital
Medical University, Beijing, China
Neurological Imaging Center, Beijing Neurosurgical Institute,
Capital Medical University, Beijing, China
Department of Neurosurgery, Beijing Tiantan Hospital, Capital
Medical University, 6 Tiantanxili, Beijing 100050, China
Centre of Brain Tumor, Beijing Institute for Brain Disorders,
China National Clinical Research Center for Neurological Diseases,
European Radiology (2018) 28:2960–2968