Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas

Abstract

BackgroundDetermining the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, which is a predictor of response to standard radiotherapy treatment in patients with glioblastoma (GBM), remains an invasive and time-consuming process. Magnetic resonance imaging (MRI)-based radiomics studies have used machine learning models as a non-invasive and time-saving approach to predict MGMT methylation status. However, more studies need to use radiomics features extracted from Diffusion-Tensor Imaging (DTI) and Dynamic Susceptibility Contrast (DSC) scans that have shown benefits in GBM patients. Therefore, we aimed to use the radiomics features from conventional and advanced MRI scans, including DSC and DTI, to build an effective model for predicting MGMT methylation status. Materials and MethodsWe enrolled 182 GBM patients whose data were obtained from The Cancer Imaging Archive (TCIA). From the initial 4514 radiomics features (1,644 conventional MRI-based and 2,870 advanced MRI-based), we used a two-stage feature selection method to select the top conventional and advanced features to build Conventional and Advanced predictive models, respectively. Besides, our Full model was developed from combining all these features. We evaluated the performances of our models and compared them with that of a “state-of-the-art” model reconstructed by re-running a flowchart from a previous study, which demonstrated the highest performance in predicting MGMT methylation status, using our dataset for the rebuilding process. ResultsThe Conventional model yielded 0.71 sensitivity, 0.78 specificity, and 0.75 accuracy, while the Advanced model scored 0.67, 0.74, and 0.71, respectively. The prior state-of-the-art model achieved 0.65, 0.57, and 0.62. Our Full model outperformed them with 0.78, 0.84, and 0.80 and had the highest AUC value (0.81) compared to Conventional (0.78) and previous (0.68) models. ConclusionOur predictive model, which incorporates radiomics features from conventional MRI scans and advanced DSC and DTI imaging, offers an improved approach to predicting the MGMT methylation status in GBM patients.