Application of Cluster Analysis of Time Evolution for Magnetic Resonance Imaging -Derived Oxygen Extraction Fraction Mapping: A Promising Strategy for the Genetic Profile Prediction and Grading of Glioma - Shen, Nanxi; Zhang, Shun; Cho, Junghun; Li, Shihui; Zhang, Ju; Xie, Yan; Wang, Yi; Zhu, Wenzhen (Frontiers in Neuroscience, 2021)
Background: The intratumoral heterogeneity of oxygen metabolism and angiogenesis are core hallmarks of glioma, unveiling genetic aberrations associated with MRI phenotypes may aid in the diagnosis and treatment of glioma. Objective: To explore the predictability of MRI-based oxygen extraction fraction mapping using cluster analysis of time evolution (CAT) for genetic profiling and gliomas grading. Methods: 91 patients with histopathologically confirmed glioma were examined with cluster analysis of time evolution for quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude-based oxygen extraction fraction (OEF) mapping and DCE-MRI. Imaging biomarkers, including oxygen metabolism (OEF) and angiogenesis (volume transfer constant [Ktrans], cerebral blood volume [CBV], and cerebral blood flow [CBF]), were investigated to predict IDH mutation, MGMT promoter methylation status, receptor tyrosine kinase (RTK) subgroup, and differentiation of glioblastoma (GBM) versus lower-grade glioma (LGG). The corresponding DNA sequencing was also obtained. Results were compared with DCE-MRI using ROC analysis. Results: IDH1-mutated lower-grade gliomas exhibited significantly lower OEF and hypoperfusion than IDH wild-type tumors (all P < .01). OEF and perfusion metrics showed a tendency toward higher values in MGMT unmethylated glioblastoma but only OEF retained significance (P = .01). Relative prevalence of RTK alterations were associated with increased OEF (P = .003) and perfusion values (P < .05). ROC analysis suggested OEF achieved best performance for IDH mutation detection (AUC = 0.828). None of the investigated parameters enabled prediction of MGMT status except OEF with a moderate AUC of 0.784. Predictive value for RTK subgroup was acceptable by using OEF (AUC = 0.764) and CBV (AUC = 0.754). OEF and perfusion metrics demonstrated excellent performance in gliomas grading. Moreover, mutational landscape revealed hypoxia or angiogenesis-relevant gene signatures were associated with specific imaging phenotypes. Conclusion: Cluster analysis of time evolution for MRI-based OEF mapping is a promising technology for oxygen measurement, and along with perfusion MRI can predict genetic profiles and tumor grade in a noninvasive and clinically relevant manner. Clinical Impact: Physiological imaging provide an in vivo portrait of genetic alterations in glioma and offer a potential strategy for noninvasively selecting patients for individualized therapies.