Non-Invasive MGMT Status Prediction in GBM Cancer Using Magnetic Resonance Images (MRI) Radiomics Features: Univariate and Multivariate Machine Learning Radiogenomics Analysis

TL;DR

This study demonstrates that machine learning analysis of MRI radiomics features can non-invasively predict MGMT methylation status in glioblastoma patients, aiding personalized treatment planning.

Contribution

It introduces a radiomics-based machine learning framework for MGMT status prediction using MRI, with specific feature selection and classifier strategies showing promising results.

Findings

Univariate analysis identified GLCM Inverse Variance as a predictor with AUC 0.71.

Multivariate models achieved up to AUC 0.78 in edema regions.

Machine learning radiomics can effectively predict MGMT methylation non-invasively.

Abstract

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