The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas

Abstract

Background. The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. Purpose. To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. Materials and Methods. A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7: 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. Results. The radiomic model showed good discrimination in both the primary dataset (AUC=0.87, 95% CI: 0.754 to 0.855, ACC=0.798, sensitivity=85.5%, specificity=75.4%, positive predictive value=0.734, and negative predictive value=0.867) and the validation dataset (AUC=0.86, 95% CI: 0.690 to 0.913, ACC=0.789, sensitivity=91.3%, specificity=69.0%, positive predictive value=0.700, and negative predictive value=0.909). Conclusion. The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.