Τρίτη 17 Απριλίου 2018

Contrast enhancement predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study

Abstract

Introduction

To assess the predictive value of magnetic resonance imaging (MRI) gadolinium enhancement as a prognostic factor in the 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups.

Methods

Four-hundred fifty patients with histopathologically confirmed glioma were retrospectively assessed between 07/1997 and 06/2014 using gadolinium enhancement, survival, and relevant prognostic molecular data [isocitrate dehydrogenase (IDH); alpha-thalassemia/mental retardation syndrome X-linked (ATRX); chromosome 1p/19q loss of heterozygosity; and O6-methylguanine DNA methyltransferase (MGMT)]. The Kaplan–Meier method was used to assess univariate survival data. A multivariate Cox proportional hazards model was performed on significant results from the univariate analysis.

Results

There were significant differences in survival between patient age (p < 0.0001), WHO glioma grades (p < 0.0001), and integrated molecular profiles (p < 0.0001). Patients with IDH1/2 mutation, loss of ATRX expression, and methylated MGMT promoter showed significantly better survival than those with the IDHwild-type (p < 0.0001), retained ATRX expression (p < 0.0001), and unmethylated MGMT promoter (p = 0.019). Survival was significantly better in patients without gadolinium enhancement (p = 0.009) who were in the IDHwild-type glioma and glioma with retained ATRX expression groups (p = 0.018 and 0.030, respectively).

Conclusions

In univariate analysis, the presence of gadolinium enhancement on preoperative MRI scans is an unfavorable factor for survival. Regarding the molecular subgroups, gadolinium enhancement is an unfavorable prognostic factor in gliomas with IDHwild-type and those with ATRX retention. However, in multivariate analysis only patient age, IDH1/2 mutation status, MGMT promoter methylation status, and WHO grade IV are relevant for predicting survival.



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