Identification of intrinsic imaging subtypes using clustering analysis based on dynamic contrast-enhanced magnetic...

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.

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.

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.

The intrinsic imaging subtypes obtained from DCE-MRI radiomics features provide a new insight into glioma classification, potentially guiding the diagnosis.