• John Maynard, Sachi Okuchi, Stephen Wastling, Ayisha Al Busaidi, Ofran Almossawi, Wonderboy Mbatha, Sebastian Brandner, Zane Jaunmuktane, Ali Murat Koc, Laura Mancini, Rolf Jäger, and Stefanie Thust.
• From the Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, UCL Institute of Neurology, London, England (J.M., S.O., S.W., L.M., R.J., S.T.); Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, England (S.W., A.A.B., W.M., A.M.K., L.M., R.J., S.T.); Population, Policy and Practice Research Unit, UCL Great Ormond Street Institute of Child Health, London, England (O.A.); Department of Neurodegenerative Disease, UCL Institute of Neurology and Division of Neuropathology, National Hospital for Neurology and Neurosurgery, London, England (S.B., Z.J.); Department of Radiology, Izmir Bozyaka Education and Research Hospital, Izmir, Turkey (A.M.K.); and Department of Imaging, University College London Foundation Hospital, London, England (R.J., S.T.).
• Radiology. 2020 Jul 1; 296 (1): 111-121.

AbstractBackground A readily implemented MRI biomarker for glioma genotyping is currently lacking. Purpose To evaluate clinically available MRI parameters for predicting isocitrate dehydrogenase (IDH) status in patients with glioma. Materials and Methods In this retrospective study of patients studied from July 2008 to February 2019, untreated World Health Organization (WHO) grade II/III gliomas were analyzed by three neuroradiologists blinded to tissue results. Apparent diffusion coefficient (ADC) minimum (ADCmin) and mean (ADCmean) regions of interest were defined in tumor and normal appearing white matter (ADCNAWM). A visual rating of anatomic features (T1 weighted, T1 weighted with contrast enhancement, T2 weighted, and fluid-attenuated inversion recovery) was performed. Interobserver comparison (intraclass correlation coefficient and Cohen κ) was followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between ADC metrics and glioma genotypes, including Bonferroni correction for multiple testing. Descriptors with sufficient concordance (intraclass correlation coefficient, >0.8; κ > 0.6) underwent univariable analysis. Predictive variables (P < .05) were entered into a multivariable logistic regression and tested in an additional test sample of patients with glioma. Results The study included 290 patients (median age, 40 years; interquartile range, 33-52 years; 169 male patients) with 82 IDH wild-type, 107 IDH mutant/1p19q intact, and 101 IDH mutant/1p19q codeleted gliomas. Two predictive models incorporating ADCmean-to-ADCNAWM ratio, age, and morphologic characteristics, with model A mandating calcification result and model B recording cyst formation, classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% confidence interval [CI]: 0.91, 0.97) and 0.96 (95% CI: 0.93, 0.98), respectively. In the test sample of 49 gliomas (nine IDH wild type, 21 IDH mutant/1p19q intact, and 19 IDH mutant/1p19q codeleted), the classification accuracy was 40 of 49 gliomas (82%; 95% CI: 71%, 92%) for model A and 42 of 49 gliomas (86%; 95% CI: 76%, 96%) for model B. Conclusion Two algorithms that incorporated apparent diffusion coefficient values, age, and tumor morphologic characteristics predicted isocitrate dehydrogenase status in World Health Organization grade II/III gliomas on the basis of standard clinical MRI sequences alone. © RSNA, 2020 Online supplemental material is available for this article.

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