Modelling MR and clinical features in grade II/III astrocytomas to predict IDH mutation status

Abstract

Background and purposeThere is increasing evidence that many IDH wildtype (IDHwt) astrocytomas have a poor prognosis and although MR features have been identified, there remains diagnostic uncertainty in the clinic. We have therefore conducted a comprehensive analysis of conventional MR features of IDHwt astrocytomas and performed a Bayesian logistic regression model to identify critical radiological and basic clinical features that can predict IDH mutation status. Materials and methods146 patients comprising 52 IDHwt astrocytomas (19 WHO Grade II diffuse astrocytomas (A II) and 33 WHO Grade III anaplastic astrocytomas (A III)), 68 IDHmut astrocytomas (53 A II and 15 A III) and 26 GBM were studied. Age, sex, presenting symptoms and Overall Survival were recorded. Two neuroradiologists assessed 23 VASARI imaging descriptors of MRI features and the relation between IDH mutation status and MR and basic clinical features was modelled by Bayesian logistic regression, and survival by Kaplan-Meier plots. ResultsThe features of greatest predictive power for IDH mutation status were, age at presentation (OR = 0.94 +/−0.03), tumour location within the thalamus (OR = 0.15 +/−0.25), involvement of speech receptive areas (OR = 0.21 +/−0.26), deep white matter invasion of the brainstem (OR = 0.10 +/−0.32), and T1/FLAIR signal ratio (OR = 1.63 +/−0.64). A logistic regression model based on these five features demonstrated excellent out-of-sample predictive performance (AUC = 0.92 +/−0.07; balanced accuracy 0.81 +/−0.09). Stepwise addition of further VASARI variables did not improve performance. ConclusionFive demographic and VASARI features enable excellent individual prediction ofIDH mutation status, opening the way to identifying patients with IDHwt astrocytomas for earlier tissue diagnosis and more aggressive management.