LAUSR

Simple Summary Glioma is the most common primary brain tumour and main cause of death of those with primary brain tumours. Multiple studies have demonstrated that glioma patients with the molecular subtype of isocitrate dehydrogenase mutation (IDH mut) combined with O6-methylguanine-DNA methyltransferase promoter methylation (MGMT meth) have good overall survival and/or progression-free survival and can benefit from temozolomide (TMZ) chemotherapy. However, this predictor is obtained through invasive pathologic methods. Radiomics can noninvasively excavate high-throughput features based on preoperative MRI images, and a radiomics marker mapping to a tumour molecular marker can be constructed through reasonable algorithms. The objective of this study was to establish a model for predicting the molecular subtype of isocitrate dehydrogenase mutation combined with O6-methylguanine-DNA methyltransferase promoter methylation in gliomas using a noninvasive radiomics model based on preoperative MRI. The model can effectively provide an important auxiliary value for the accurate diagnosis of tumour molecular typing, decision-making of the chemotherapy drug temozolomide and prognosis assessment in clinical management. Abstract Background: The molecular subtype of IDH mut combined with MGMT meth in gliomas suggests a good prognosis and potential benefit from TMZ chemotherapy. The aim of this study was to establish a radiomics model to predict this molecular subtype. Method: The preoperative MR images and genetic data of 498 patients with gliomas were retrospectively collected from our institution and the TCGA/TCIA dataset. A total of 1702 radiomics features were extracted from the tumour region of interest (ROI) of CE-T1 and T2-FLAIR MR images. Least absolute shrinkage and selection operator (LASSO) and logistic regression were used for feature selection and model building. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Results: Regarding clinical variables, age and tumour grade were significantly different between the two molecular subtypes in the training, test and independent validation cohorts (p < 0.05). The areas under the curve (AUCs) of the radiomics model based on 16 selected features in the SMOTE training cohort, un-SMOTE training cohort, test set and independent TCGA/TCIA validation cohort were 0.936, 0.932, 0.916 and 0.866, respectively, and the corresponding F1-scores were 0.860, 0.797, 0.880 and 0.802. The AUC of the independent validation cohort increased to 0.930 for the combined model when integrating the clinical risk factors and radiomics signature. Conclusions: radiomics based on preoperative MRI can effectively predict the molecular subtype of IDH mut combined with MGMT meth.