  Background and aim: This study aimed to predict methylation status of the O-6
methyl guanine-DNA methyl transferase (MGMT) gene promoter status by using MRI
radiomics features, as well as univariate and multivariate analysis.
  Material and Methods: Eighty-two patients who had a MGMT methylation status
were include in this study. Tumor were manually segmented in the four regions
of MR images, a) whole tumor, b) active/enhanced region, c) necrotic regions
and d) edema regions (E). About seven thousand radiomics features were
extracted for each patient. Feature selection and classifier were used to
predict MGMT status through different machine learning algorithms. The area
under the curve (AUC) of receiver operating characteristic (ROC) curve was used
for model evaluations.
  Results: Regarding univariate analysis, the Inverse Variance feature from
gray level co-occurrence matrix (GLCM) in Whole Tumor segment with 4.5 mm Sigma
of Laplacian of Gaussian filter with AUC: 0.71 (p-value: 0.002) was found to be
the best predictor. For multivariate analysis, the decision tree classifier
with Select from Model feature selector and LOG filter in Edema region had the
highest performance (AUC: 0.78), followed by Ada Boost classifier with Select
from Model feature selector and LOG filter in Edema region (AUC: 0.74).
  Conclusion: This study showed that radiomics using machine learning
algorithms is a feasible, noninvasive approach to predict MGMT methylation
status in GBM cancer patients
  Keywords: Radiomics, Radiogenomics, GBM, MRI, MGMT
