MRI-Based Radiomics Approach with Deep Learning for Distinguishing IDH-Mutant from IDH Wild-type Grade-4 Astrocytomas

Patients (n=57) with isocitrate dehydrogenase (IDH) mutant grade-4 astrocytomas and IDH wild-type glioblastomas underwent anatomical imaging (post-contrast T1 and T2-FLAIR) on 3T magnet. Neoplasms were segmented into 5 ROIs and 105 radiomics features were extracted from each ROI. Features were subsequently selected using various algorithms. Patients were divided into two groups (50%-training and 50%-testing). A GAN-based deep learning algorithm was used to generate 1000 synthesized data-sets and four distinct deep-learning modules were implemented. The best model for differentiating two genotypes of neoplasms was obtained from core tumor regions by using K-best feature selection and Ensembled algorithm with high diagnostic performance.