Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients

Gliomas are the most common cause of mortality among primary brain tumors. Molecular markers, including Isocitrate Dehydrogenase (IDH) and O[6]-methylguanine-DNA methyltransferase (MGMT) influence treatment responses and prognosis. Deep learning (DL) models may provide a non-invasive method for predicting the status of these molecular markers. To achieve non-invasive determination of gene mutations in glioma patients, we compare 2D and 3D ResNet models to predict IDH and MGMT status, using T1, post-contrast T1, and FLAIR MRI sequences. USCF glioma dataset was used, which contains 495 patients with known IDH and 410 patients with known MGMT status. The dataset was divided into training (60%), tuning (20%), and test (20%) subsets at the patient level. The 2D models take axial, coronal, and sagittal tumor slices as three separate models. To ensemble the 2D predictions the three different views were combined using logistic regression. Various ResNet architectures (ResNet10, 18, 34, 50, 101, 152) were trained. For the 3D approach, we incorporated the entire brain tumor volume in the ResNet10, 18, and 34 models. After optimizing each model, the models with the lowest tuning loss were selected for further evaluation on the separate test sets. The best-performing models in IDH prediction were the 2D ResNet50, achieving a test area under the receiver operating characteristic curve (AUROC) of 0.9096, and the 3D ResNet34, which reached a test AUROC of 0.8999. For MGMT status prediction, the 2D ResNet152 achieved a test AUROC of 0.6168; however, all 3D models yielded AUROCs less than 0.5. Overall, the study indicated that both 2D and 3D models showed high predictive value for IDH prediction, with slightly better performance in 2D models.