Abstract
BACKGROUND: There has been no research based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for the stratification diagnosis and prognostic evaluation of gliomas. The study aimed to identify multiple glioma subtypes and decipher the gene expression profiles linked with different subtypes. METHODS: Cross-sectional and retrospective data of 189 patients were collected. The static radiomics features were obtained at three time points (0, 90, and 300 s) corresponding to pre-contrast, arterial, and delayed phases, respectively. The dynamic radiomics features were retrieved by determining the temporal anisotropy of these three phases. Multi-omics clustering was used to identify intrinsic radiomics subtypes within the cohort. The association between the radiomics clusters and gene expression profiles was evaluated through the analysis of variance. RESULTS: The patients in cluster 3 were oldest. Cluster 3 and cluster 1 had higher frequency of grade 4, high Ki-67 level, glioblastoma isocitrate dehydrogenase (IDH) wild-type, and unmethylated O6-methylguanine-DNA methyltransferase (MGMT) promoter. Cluster 3 had the highest frequency of epidermal growth factor receptor (EGFR) amplification and cyclin-dependent kinase inhibitor (CDKN) 2A/B homozygous deletion. Cluster 1 had the highest frequency of EGFR non-mutant. Cluster 4 and cluster 2 had a higher frequency of astrocytoma IDH-mutant. Cluster 4 had a higher frequency of grade 3, oligodendroglioma IDH-mutant and 1p/19q codeleted, MGMT promoter methylation, and EGFR non-amplification. Cluster 2 had a higher frequency of grade 2, low Ki-67 level, and patients without CDKN 2A/B homozygous deletion. There were no associations for other molecular markers between clusters. CONCLUSIONS: The intrinsic imaging subtypes obtained from DCE-MRI radiomics features provide a new insight into glioma classification, potentially guiding the diagnosis.