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

Abstract

Background and purpose: There 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 Methods: 146 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. • Results: The 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. • Conclusion: Five demographic and VASARI features enable excellent individual prediction of IDH mutation status, opening the way to identifying patients with IDHwt astrocytomas for earlier tissue diagnosis and more aggressive management.

Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning

“…IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, and triggered the integrated genomic-histological characterization of brain tumours proposed in the 2016 World Health Organization (WHO) classification system 1 . Recently, some studies have shown IDH mutational status may be predicted using neuroimaging with good accuracy (between 78.2% and 92.8%) [11][12][13][14][15][16][17][18][19][20] , and also with very good diagnostic performance when using 2-hydroxyglutarate MR spectroscopy (2HG-MRS, with a pooled 91% sensitivity and 95% specificity) 21,22 . However, neuroimaging is not yet state-of-the-art in detecting IDH mutations in glioma, which is one of the reasons tumour sampling is often still necessary, also because surgical resection/debulking is part of the current mainstay of treatment 23 .…”

“…However, it is not clear how the patients were selected in that study. Furthermore, the performance of previous deep learning methods on either MRI or H&E slides remains unclear because of the small sample sizes and unbalanced sample distributions in past studies [11][12][13][14][15][16][17][18][19][20] .…”

Regional and Volumetric Parameters for Diffusion-Weighted WHO Grade II and III Glioma Genotyping: A Method Comparison

“…Of these, conventional MR imaging has the advantage of universal availability, but mostly provides visual-anatomic features, some of which have limited reproducibility. 9,10 Advanced MR imaging techniques such as perfusion and spectroscopy provide physiologic, quantifiable tumor data but can have threshold overlap and lack of technical standardization. 11 DWI is widely integrated into clinical glioma MR imaging protocols with tissue properties measurable at the time of reporting.…”

Exploratory Analysis of Qualitative MR Imaging Features for the Differentiation of Glioblastoma and Brain Metastases

“…The VASARI MR feature guide has been developed for primary brain tumors and has mostly been applied to GBMs (13, [18][19][20][21][22][23][24] and in a few studies to lower grade gliomas (26)(27)(28). To the best of our knowledge, this is the first study applying VASARI features to BMs.…”